Chapter 4
2023-08-29
Load Packages
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(meta)## Loading 'meta' package (version 6.5-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drslibrary(PRISMAstatement)
library(skimr)
library(MASS)##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## selectlibrary(ggpubr)setwd("~/Desktop/Chapter 4")
photo <- read.csv("observations_2022.csv")
summary(photo)## region site site_code microsite
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month day
## Min. :1.000 Min. :1.000 Length:58015 Min. : 3.00
## 1st Qu.:1.000 1st Qu.:1.000 Class :character 1st Qu.: 6.00
## Median :1.000 Median :2.000 Mode :character Median :16.00
## Mean :1.775 Mean :1.527 Mean :13.98
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:22.00
## Max. :4.000 Max. :2.000 Max. :29.00
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:58015
## 1st Qu.:2022 1st Qu.: 0.000 1st Qu.: 5386 Class :character
## Median :2022 Median : 0.000 Median :12638 Mode :character
## Mean :2022 Mean : 4.681 Mean :15170
## 3rd Qu.:2022 3rd Qu.:11.000 3rd Qu.:24318
## Max. :2022 Max. :14.000 Max. :38822
## filename timestamp animal.hit class
## Length:58015 Length:58015 Min. :0.00000 Length:58015
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.06521
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:58015 Min. : 1.000
## Class :character 1st Qu.: 1.000
## Mode :character Median : 1.000
## Mean : 1.001
## 3rd Qu.: 1.000
## Max. :12.000photo <- photo %>%
filter(common_name != "Human")
photo <- photo %>%
filter(common_name != "Human-Camera Trapper")
photo <- photo %>%
filter(common_name != "Domestic Dog")
photo <- photo %>%
filter(common_name != "Vehicle")
photo <- photo %>%
dplyr::filter(common_name != "Insect")
photo <- photo %>%
dplyr::filter(common_name != "Animal")
photo <- photo %>%
dplyr::filter(common_name != "Bird")
photo <- photo %>%
filter(common_name != "No CV Result")
count.hit <- photo %>%
count(animal.hit) %>%
na.omit()
summary(count.hit)## animal.hit n
## Min. :0.00 Min. : 3169
## 1st Qu.:0.25 1st Qu.:15935
## Median :0.50 Median :28700
## Mean :0.50 Mean :28700
## 3rd Qu.:0.75 3rd Qu.:41466
## Max. :1.00 Max. :54232### 2022 Had a 5.88% catch rate### Animal Observations by Site_Code
animals_by_sitecode <- photo%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode <- animals_by_sitecode %>%
filter(common_name != "Blank") ### Animal observations by Site
animals_by_site <- photo %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site <- animals_by_site %>% filter(common_name != "Blank")### Animal observations by Density
animals_by_density <- photo %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density <- animals_by_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")### Total Observations 2022
Total_Observations <- photo %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")density_obvs <- merge(animals_by_density, Total_Observations, all = TRUE)
density_obvs$percent_presence <- density_obvs$captures/density_obvs$total### Percent proportion Figure
plot1 <- ggplot(density_obvs, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot1 + scale_fill_manual(values = c("#009900", "#0066cc"))
library(emmeans)
m1 <- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs)
anova(m1, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 44 22806
## microsite 1 5.4 43 22801 0.01984 *
## common_name 26 22800.9 17 0 < 2e-16 ***
## microsite:common_name 17 0.0 0 0 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e1 <- emmeans(m1, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactions#head(e1)animals_density <- photo %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density <- animals_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")All current PCOA code for 2022 only
### PCA
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(ape) ### For PCOA##
## Attaching package: 'ape'## The following object is masked from 'package:ggpubr':
##
## rotate## The following object is masked from 'package:dplyr':
##
## wherepca_data <- animals_density ### Created new df for pca data
pca_data <- pca_data %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data)## [1] 22 28env <- read.csv("environment.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)## [1] 22 5m01 <- adonis(pca_data ~ microsite*shrub_density, data = env)## 'adonis' will be deprecated: use 'adonis2' insteadm01## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2242 0.22425 0.76358 0.03600 0.592
## shrub_density 1 0.4245 0.42452 1.44550 0.06815 0.199
## Residuals 19 5.5799 0.29368 0.89584
## Total 21 6.2287 1.00000
##
## $call
## adonis(formula = pca_data ~ microsite * shrub_density, data = env)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -4.757692 5.000000e-02 -13.455128
## microsite1 -6.857692 -5.000000e-02 -18.955128
## shrub_density 1.876923 1.823528e-17 4.184615
## microsite1:shrub_density NA NA NA
## Blunt-nosed Leopard Lizard Bobcat
## (Intercept) 0.9987179 -0.6602564
## microsite1 0.8987179 -1.1602564
## shrub_density -0.1538462 0.1692308
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) 0.18717949 20.411538
## microsite1 -0.21282051 13.511538
## shrub_density 0.06153846 -1.815385
## microsite1:shrub_density NA NA
## California Pocket Mouse California Quail
## (Intercept) 0.35641026 -4.2487179
## microsite1 0.15641026 -5.6487179
## shrub_density -0.03076923 0.9538462
## microsite1:shrub_density NA NA
## California Thrasher Common Raven Coyote
## (Intercept) -1.4743590 0.7910256 1.4923077
## microsite1 -1.4743590 -3.5089744 -4.1076923
## shrub_density 0.2769231 0.3230769 0.4769231
## microsite1:shrub_density NA NA NA
## Desert Cottontail Desert Iguana Giant Kangaroo Rat
## (Intercept) -24.729487 2.07820513 6.9935897
## microsite1 -26.629487 -1.82179487 4.4935897
## shrub_density 4.861538 -0.01538462 -0.7692308
## microsite1:shrub_density NA NA NA
## Great White Egret Greater Roadrunner
## (Intercept) 5.000000e-02 -4.3730769
## microsite1 -5.000000e-02 -4.4730769
## shrub_density 1.076807e-17 0.8307692
## microsite1:shrub_density NA NA
## Heermann's Kangaroo Rat Killdeer Kit Fox
## (Intercept) 140.330769 5.000000e-02 0.44487179
## microsite1 59.130769 -5.000000e-02 0.14487179
## shrub_density -7.707692 6.937479e-18 -0.01538462
## microsite1:shrub_density NA NA NA
## Lark Sparrow Loggerhead Shrike Mohave Ground Squirrel
## (Intercept) -1.8384615 -0.5935897 0.81923077
## microsite1 -2.2384615 -0.8935897 0.71923077
## shrub_density 0.3846154 0.1692308 -0.09230769
## microsite1:shrub_density NA NA NA
## Mourning Dove Nelson's Antelope Squirrel
## (Intercept) -1.9384615 18.984615
## microsite1 -2.1384615 13.784615
## shrub_density 0.3846154 -2.246154
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Vesper Sparrow
## (Intercept) -0.5641026 5.000000e-02 5.000000e-02
## microsite1 -0.5641026 -5.000000e-02 -5.000000e-02
## shrub_density 0.1076923 1.076807e-17 6.937479e-18
## microsite1:shrub_density NA NA NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.72187241 0.84634032 1.11978420 0.78585052
## microsite1 0.19194029 0.33115230 0.42744428 0.26477687
## shrub_density -0.03063205 -0.05640075 -0.06512504 -0.03711672
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.7805666 0.70692759 0.95262164 0.80543797
## microsite1 0.2293742 0.15739580 0.22071669 0.25186788
## shrub_density -0.0393115 -0.02414532 -0.02857079 -0.03727318
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7428122 1.4970441 0.99852391 1.06366871
## microsite1 1.0220992 0.8596173 0.44864549 0.53735308
## shrub_density -0.1847971 -0.1548735 -0.07052619 -0.08656954
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.82385406 0.795551518 0.79238103 0.84065136
## microsite1 0.12690322 0.021850057 0.21124835 0.30308675
## shrub_density -0.02568071 -0.008853955 -0.02915258 -0.04325977
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.38555800 0.50980072 0.68786923 0.55515661
## microsite1 -0.59216512 -0.43255379 -0.20343288 -0.30788751
## shrub_density 0.09323238 0.06736799 0.02712249 0.04455634
## microsite1:shrub_density NA NA NA NA
## 21 22
## (Intercept) 0.881696341 0.64736295
## microsite1 0.013954406 -0.20302121
## shrub_density -0.001421805 0.02954886
## microsite1:shrub_density NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.51232234 2.473580584
## [2,] 0.49704227 1.354522877
## [3,] 0.52697747 2.999162560
## [4,] 0.50159378 1.112610936
## [5,] 0.35748234 0.433008035
## [6,] 0.81978454 1.143168562
## [7,] 0.59669034 0.268670214
## [8,] 1.00688936 1.029652890
## [9,] 1.15780782 0.858601644
## [10,] 3.89781386 0.644123834
## [11,] 0.52030537 1.698905230
## [12,] 1.62795975 0.978406120
## [13,] 1.00678867 0.844647685
## [14,] 0.53481867 0.176443763
## [15,] 1.44174975 0.594217705
## [16,] 0.61611860 0.989803128
## [17,] 0.91864835 0.286766250
## [18,] 2.64497631 0.673405539
## [19,] 0.42536604 0.681951346
## [20,] 1.73878143 0.374036871
## [21,] 1.10221417 2.093689181
## [22,] 0.63496049 0.509472870
## [23,] 1.09809428 1.196762517
## [24,] 2.21326320 0.832747600
## [25,] 0.51845280 0.329327758
## [26,] 0.68178433 0.978852789
## [27,] 1.00707132 0.852385361
## [28,] 2.65152874 1.577666803
## [29,] 0.71714657 0.377430552
## [30,] 1.29562492 1.000394381
## [31,] 0.44634205 1.535223223
## [32,] 0.65175149 0.950133556
## [33,] 0.83122592 0.712003997
## [34,] 0.83901689 0.709685482
## [35,] 0.35144226 1.261103502
## [36,] 0.97423126 0.486399131
## [37,] 1.41654411 1.139195360
## [38,] 2.63183509 0.822422225
## [39,] 0.63045381 1.048812011
## [40,] 0.57146521 0.235203844
## [41,] 0.58255103 1.075553654
## [42,] 0.83195385 3.509805522
## [43,] 0.43876560 0.674938357
## [44,] 3.03205793 1.528627198
## [45,] 0.73541826 0.548975511
## [46,] 0.81734820 1.135478497
## [47,] 0.66520515 0.939356479
## [48,] 2.22233465 0.835377447
## [49,] 1.03708524 1.225118334
## [50,] 0.24051637 0.002562186
## [51,] 0.44912974 1.786818579
## [52,] 1.09840206 0.777684139
## [53,] 0.12242519 1.136480694
## [54,] 0.94624480 0.281972197
## [55,] 0.85764049 1.098086792
## [56,] 0.92945815 0.727322182
## [57,] 1.12233361 1.710313997
## [58,] 0.34049949 2.396354200
## [59,] 0.21264498 1.014498396
## [60,] 0.42134724 0.901190137
## [61,] 1.07287911 0.754105954
## [62,] 1.55224313 2.083504836
## [63,] 0.54034848 0.520172598
## [64,] 0.58094094 1.124299095
## [65,] 0.44621573 1.405196331
## [66,] 0.75839148 1.455369432
## [67,] 0.69548753 1.645188107
## [68,] 0.67580723 1.139070681
## [69,] 1.02986205 0.563966151
## [70,] 0.73676292 0.680581554
## [71,] 0.30295915 0.208467470
## [72,] 0.75661983 0.517700811
## [73,] 1.22171119 1.806591956
## [74,] 1.22569404 1.518518351
## [75,] 1.14363340 0.983341434
## [76,] 0.55592047 1.575122426
## [77,] 1.06294668 0.558330705
## [78,] 1.37037649 0.320916716
## [79,] 1.89467743 0.872142426
## [80,] 0.67500443 1.761222143
## [81,] 1.28675605 2.417388870
## [82,] 1.54805396 0.698164543
## [83,] 1.45547508 2.028182509
## [84,] 0.58763708 0.599658980
## [85,] 0.92807687 1.709026930
## [86,] 0.64005088 0.740102236
## [87,] 0.54578394 0.801595372
## [88,] 1.79599479 1.500531928
## [89,] 0.90133443 0.703227840
## [90,] 0.69936616 2.191048073
## [91,] 0.93875726 0.596185445
## [92,] 0.75607402 0.525345798
## [93,] 0.34584872 0.741909916
## [94,] 0.76470944 1.565698144
## [95,] 0.63404972 0.454078514
## [96,] 0.50472178 0.310272503
## [97,] 1.16018927 1.366538857
## [98,] 0.08559623 0.654547986
## [99,] 0.62574935 1.697578182
## [100,] 1.53250519 0.530083411
## [101,] 0.77964609 1.022860016
## [102,] 0.16220950 1.118423459
## [103,] 0.22724450 1.183286670
## [104,] 0.97820090 2.323884974
## [105,] 1.27440256 0.898004413
## [106,] 0.52178300 2.160333342
## [107,] 0.60915301 0.634713157
## [108,] 0.97531782 0.774424849
## [109,] 1.02979915 1.996457404
## [110,] 1.21128766 0.661318725
## [111,] 1.13742991 0.794475584
## [112,] 1.49480314 1.257785092
## [113,] 0.30630329 1.819060124
## [114,] 1.04313623 0.190612673
## [115,] 1.12720208 0.743063741
## [116,] 1.71456332 0.764395268
## [117,] 0.82433685 0.423892531
## [118,] 0.58157710 1.630981442
## [119,] 0.50645232 0.588688186
## [120,] 0.87989761 2.113158815
## [121,] 0.74103795 1.121913244
## [122,] 0.20549892 0.790080489
## [123,] 2.71337090 0.404841827
## [124,] 2.08278409 0.653325661
## [125,] 0.93174442 0.681066462
## [126,] 0.53060081 0.640613406
## [127,] 0.64917668 0.479552931
## [128,] 0.49404401 1.190187002
## [129,] 0.21844911 0.413878150
## [130,] 0.81111435 0.580928697
## [131,] 2.47704413 0.527467157
## [132,] 1.24331927 1.071854038
## [133,] 0.42672390 0.456200346
## [134,] 0.41211975 0.337075645
## [135,] 0.77147751 1.754845752
## [136,] 2.80238185 0.980626951
## [137,] 0.70120935 0.489127611
## [138,] 0.51760489 0.397512047
## [139,] 1.33572607 0.775856272
## [140,] 0.86623847 0.792813953
## [141,] 0.21844221 1.167942045
## [142,] 0.54186621 1.790142643
## [143,] 0.61618076 0.262524246
## [144,] 0.47844914 0.631887512
## [145,] 1.50828340 0.959079815
## [146,] 1.98948838 0.598728907
## [147,] 0.73863087 1.181904896
## [148,] 0.96532303 0.590855456
## [149,] 0.67078956 0.542555851
## [150,] 0.51252224 0.585690971
## [151,] 1.53080097 0.618156610
## [152,] 1.02904577 0.978899670
## [153,] 0.67012349 0.991294697
## [154,] 0.90908833 0.451544514
## [155,] 0.28180438 1.288455029
## [156,] 2.61339834 0.597262362
## [157,] 0.27143583 1.082892473
## [158,] 0.89221906 0.229567738
## [159,] 0.62089282 1.734210019
## [160,] 0.84474508 0.761334031
## [161,] 2.91762355 0.325307744
## [162,] 1.03787067 2.799887781
## [163,] 2.40749044 0.734041427
## [164,] 1.00450343 1.353985666
## [165,] 1.58608192 1.623462864
## [166,] 0.38291788 2.485125543
## [167,] 1.85305223 1.308823956
## [168,] 0.58005503 1.262691666
## [169,] 0.20021482 1.984416492
## [170,] 0.97660894 0.781709905
## [171,] 3.37901418 1.112114707
## [172,] 1.68910183 0.650252489
## [173,] 0.77721191 0.884328887
## [174,] 1.02872283 0.698492493
## [175,] 0.98789064 0.296907917
## [176,] 0.48418149 1.114408489
## [177,] 0.45361011 0.655883174
## [178,] 1.75633045 1.132250677
## [179,] 1.10427015 1.118347770
## [180,] 1.01999422 1.859835107
## [181,] 0.75777989 0.894708631
## [182,] 0.97042288 1.103463852
## [183,] 1.75662757 0.349112567
## [184,] 0.67588475 0.168502740
## [185,] 0.97684174 0.812856488
## [186,] 1.45523223 1.538975860
## [187,] 0.54463059 0.891326940
## [188,] 0.58353773 0.624505095
## [189,] 0.38845770 1.076507368
## [190,] 0.57444286 1.665662335
## [191,] 0.56686182 0.812278388
## [192,] 0.85659490 0.756985254
## [193,] 1.47935058 0.329484511
## [194,] 1.27263244 0.909294992
## [195,] 1.08360812 0.793447319
## [196,] 1.64618286 3.237726891
## [197,] 1.78992987 2.395211356
## [198,] 1.06063522 0.376182460
## [199,] 1.30894628 0.579133893
## [200,] 0.20860238 0.621766928
## [201,] 1.16333347 1.638154744
## [202,] 0.73435879 2.921689122
## [203,] 0.57943298 0.715926849
## [204,] 0.93432456 1.144944795
## [205,] 0.76370199 2.454283420
## [206,] 2.14441054 3.420120121
## [207,] 2.06441082 0.408806969
## [208,] 0.68568017 0.232972854
## [209,] 0.81965078 0.574955501
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## [652,] 0.52469436 1.945304453
## [653,] 3.86173077 1.830407538
## [654,] 1.18444809 0.435816918
## [655,] 1.82125606 2.617005566
## [656,] 0.19790057 0.523854850
## [657,] 0.70422466 0.934002010
## [658,] 1.19381733 0.696807512
## [659,] 0.46017085 1.077844559
## [660,] 0.49166720 0.535149795
## [661,] 0.42765031 0.864401187
## [662,] 3.53894419 0.265858185
## [663,] 0.98693737 0.801352247
## [664,] 1.49382127 2.041465929
## [665,] 0.61080626 0.962745067
## [666,] 2.80924718 0.114419097
## [667,] 0.38081658 0.700526462
## [668,] 2.35511039 1.590511472
## [669,] 1.24111121 0.508660361
## [670,] 0.89265876 2.114026060
## [671,] 0.36783462 0.384634402
## [672,] 0.80772536 1.553191107
## [673,] 1.68809986 1.770857074
## [674,] 0.76527208 0.664291053
## [675,] 0.65042560 0.983895433
## [676,] 2.21279937 2.256182960
## [677,] 0.45132959 1.111522643
## [678,] 1.21129480 1.491234795
## [679,] 3.86029661 0.939579205
## [680,] 0.53941227 0.538525807
## [681,] 1.14802055 1.008733964
## [682,] 0.69388371 0.773002614
## [683,] 0.74815837 0.884456863
## [684,] 1.02817493 0.406823245
## [685,] 0.92598223 0.838030747
## [686,] 0.78671274 1.055325006
## [687,] 0.76740310 0.819601236
## [688,] 1.47519178 1.306783010
## [689,] 0.28879416 0.757153374
## [690,] 0.49349823 0.918557704
## [691,] 1.55463764 0.768794823
## [692,] 1.54590316 0.995162094
## [693,] 0.46659100 0.714231968
## [694,] 0.62298735 1.168471924
## [695,] 0.79984164 0.957905993
## [696,] 0.69823892 1.566829130
## [697,] 1.47776289 0.843541260
## [698,] 1.26554627 1.051529957
## [699,] 2.29228237 1.181286276
## [700,] 0.57813712 1.102159393
## [701,] 0.92949893 1.226139196
## [702,] 1.29383506 0.667703532
## [703,] 0.82642739 0.763494542
## [704,] 1.51304866 1.042434795
## [705,] 0.15720250 0.886484947
## [706,] 1.21764429 1.109806532
## [707,] 1.07673603 0.538353400
## [708,] 0.78133022 0.701111494
## [709,] 0.73945014 1.158065790
## [710,] 0.48185453 0.797056925
## [711,] 0.80255359 2.462794004
## [712,] 0.48444344 0.506647296
## [713,] 1.45240435 1.340167974
## [714,] 0.31867188 0.933982311
## [715,] 0.57157968 0.715218068
## [716,] 0.44811540 0.654550608
## [717,] 0.88625119 0.887138772
## [718,] 0.90037483 0.703598962
## [719,] 0.43808838 1.672990188
## [720,] 0.65274042 1.591771926
## [721,] 0.51914483 0.573659051
## [722,] 0.96645381 0.886690786
## [723,] 0.82185152 1.689822869
## [724,] 1.20085730 0.993759308
## [725,] 0.67746436 0.980658805
## [726,] 0.70221970 0.675305483
## [727,] 0.56914493 1.424074742
## [728,] 0.54142720 0.287475909
## [729,] 0.34522905 0.738983544
## [730,] 0.28691538 0.823822344
## [731,] 1.10196025 0.673946309
## [732,] 1.96134238 0.744899690
## [733,] 1.42694747 0.371913136
## [734,] 1.11450657 0.931926742
## [735,] 0.64267492 1.216285455
## [736,] 0.11265274 0.658494476
## [737,] 1.62368278 1.146288287
## [738,] 1.14794272 1.318607873
## [739,] 0.70395982 1.506289178
## [740,] 0.42531375 0.327979241
## [741,] 0.51550280 0.763256612
## [742,] 0.80383533 0.725507021
## [743,] 1.17506630 0.253464135
## [744,] 2.14238048 1.605760058
## [745,] 0.68083068 0.389817186
## [746,] 1.22737174 1.070884271
## [747,] 0.92454416 0.798308093
## [748,] 0.58430427 0.530647786
## [749,] 0.84740721 1.301859486
## [750,] 0.44731215 0.601764718
## [751,] 1.58314935 0.870996911
## [752,] 0.40348286 0.581008939
## [753,] 1.03110361 1.061115687
## [754,] 1.75496135 0.750353549
## [755,] 0.65935875 1.105800871
## [756,] 1.52157572 0.650250059
## [757,] 1.01046281 1.692003061
## [758,] 0.43132005 0.898152553
## [759,] 1.40747366 1.584099949
## [760,] 0.35366270 0.483105117
## [761,] 0.82385850 0.672143885
## [762,] 0.69104532 1.086926498
## [763,] 0.91782644 0.497329476
## [764,] 0.77147637 1.378284014
## [765,] 0.86071533 0.820852883
## [766,] 0.49714206 0.532106404
## [767,] 1.16875672 1.857008308
## [768,] 0.57425543 1.183603046
## [769,] 1.35047360 1.350757411
## [770,] 1.15666508 0.151866535
## [771,] 1.90769038 0.510204848
## [772,] 1.09339788 0.772836609
## [773,] 0.58019035 0.911516816
## [774,] 0.25667812 0.518766215
## [775,] 0.79117195 0.904268388
## [776,] 0.94181564 1.359603145
## [777,] 5.52518847 0.221073796
## [778,] 0.68135300 1.696725260
## [779,] 0.92245456 1.049484243
## [780,] 1.68195247 0.930888273
## [781,] 2.12004713 0.683446144
## [782,] 0.94399667 0.798878475
## [783,] 0.80970692 0.407977775
## [784,] 0.44749460 0.762961195
## [785,] 0.25953608 0.296056352
## [786,] 0.32713801 0.733673194
## [787,] 0.54218799 1.808531355
## [788,] 2.28988886 1.332588124
## [789,] 1.31679614 1.241637145
## [790,] 0.16346977 1.178675548
## [791,] 0.24150972 1.225104840
## [792,] 0.79007722 0.759797427
## [793,] 0.22274024 0.844680771
## [794,] 0.30385244 0.233305839
## [795,] 1.49557816 0.551533783
## [796,] 0.52825890 0.920091772
## [797,] 0.68527359 0.267664319
## [798,] 0.45830871 1.292852689
## [799,] 2.90123020 2.394361313
## [800,] 0.86270874 1.882504752
## [801,] 0.80525226 0.616197208
## [802,] 0.31997388 1.170702585
## [803,] 1.72132129 1.154298317
## [804,] 1.16439418 0.531915886
## [805,] 0.84010486 1.518254169
## [806,] 0.96511560 0.944098878
## [807,] 0.75463932 1.570759284
## [808,] 1.25787354 0.308987239
## [809,] 2.10763560 1.676883188
## [810,] 1.25655583 0.771741523
## [811,] 1.20149126 0.731191059
## [812,] 0.53070646 0.769668920
## [813,] 1.69905111 0.198984027
## [814,] 2.52031926 1.324276646
## [815,] 0.40835015 1.135312240
## [816,] 0.93073208 0.546698241
## [817,] 0.91087932 1.500447019
## [818,] 0.70441165 0.263608647
## [819,] 0.32905113 1.061831700
## [820,] 0.66708075 0.382963821
## [821,] 0.14945886 1.093420688
## [822,] 2.75666456 0.158634585
## [823,] 1.33993755 0.666622728
## [824,] 1.17638970 0.708128558
## [825,] 3.77553842 0.576286528
## [826,] 0.89523937 0.542694884
## [827,] 0.88153496 0.583358632
## [828,] 1.00463222 0.970043484
## [829,] 1.60291820 0.619380405
## [830,] 0.29489239 0.975114041
## [831,] 0.44772027 0.871559185
## [832,] 0.59237202 0.724704578
## [833,] 0.33830827 0.974996922
## [834,] 0.57771522 0.894641996
## [835,] 0.89862663 1.098393444
## [836,] 1.49367706 0.116923718
## [837,] 0.85707026 0.443766232
## [838,] 1.66491824 1.303914743
## [839,] 1.34132231 0.525003449
## [840,] 0.94420586 2.379559590
## [841,] 0.75064053 0.653922143
## [842,] 0.75820232 1.091435802
## [843,] 1.03625506 1.179135151
## [844,] 0.67274508 1.681974599
## [845,] 0.90813546 0.842629672
## [846,] 1.01806207 1.448815858
## [847,] 1.73529609 0.371176157
## [848,] 1.26222111 1.488543811
## [849,] 0.51716733 0.325703798
## [850,] 0.97359801 0.704520483
## [851,] 3.96758654 1.560948963
## [852,] 0.41034869 0.796307923
## [853,] 0.90399150 1.137231132
## [854,] 1.14782493 1.510729383
## [855,] 0.44503022 1.060149094
## [856,] 0.47796841 0.556517255
## [857,] 1.48282860 0.793160690
## [858,] 1.36549478 0.865712171
## [859,] 2.67102031 2.158056387
## [860,] 0.70719133 0.243061677
## [861,] 1.72482059 0.718642013
## [862,] 0.36235592 0.453840089
## [863,] 2.56210066 2.240706731
## [864,] 0.55995507 0.998508563
## [865,] 0.87893592 0.647022620
## [866,] 0.90718386 0.295301729
## [867,] 1.07992040 1.544006792
## [868,] 0.59211841 0.800328122
## [869,] 0.72841607 2.030364131
## [870,] 2.05777125 0.757639295
## [871,] 2.87808060 0.835113742
## [872,] 1.26749428 0.566076485
## [873,] 2.27317011 0.740802373
## [874,] 1.29566331 1.040330032
## [875,] 0.27534301 1.145961875
## [876,] 1.21494590 0.620007632
## [877,] 0.50013953 0.624180629
## [878,] 0.93841010 1.225163545
## [879,] 1.63438573 1.704152125
## [880,] 1.80754685 1.427108281
## [881,] 2.49556679 3.477827991
## [882,] 1.25211336 0.131881464
## [883,] 2.26317658 0.717512260
## [884,] 0.42166616 0.646584241
## [885,] 0.79539923 0.479127674
## [886,] 1.66290424 0.448484401
## [887,] 0.92286110 0.976964434
## [888,] 0.20194136 0.623817267
## [889,] 0.89278204 1.318955318
## [890,] 0.78444176 0.289347397
## [891,] 0.65463272 1.427884782
## [892,] 1.41358014 3.052740899
## [893,] 0.92392622 1.536152620
## [894,] 0.17260471 0.961852235
## [895,] 0.46317931 1.767607852
## [896,] 0.27247005 0.913904269
## [897,] 0.56690424 0.455747271
## [898,] 0.44148609 1.221163421
## [899,] 1.37089255 1.176935831
## [900,] 0.76873569 1.238497297
## [901,] 1.69307488 3.273560778
## [902,] 0.59952479 0.248370190
## [903,] 1.26545107 0.231434942
## [904,] 0.89573753 0.820040162
## [905,] 0.82149866 0.830034568
## [906,] 0.29146458 0.994442107
## [907,] 0.92161741 0.540857683
## [908,] 0.82657083 1.130221193
## [909,] 1.07260707 1.004319383
## [910,] 0.93270484 2.844378759
## [911,] 2.14962376 0.879296573
## [912,] 1.31770177 0.414843430
## [913,] 0.69593518 1.420558814
## [914,] 0.69282340 1.951589954
## [915,] 1.55937784 0.359919899
## [916,] 2.55177865 0.608914782
## [917,] 0.83913402 1.190552600
## [918,] 0.80033774 0.806860962
## [919,] 1.82864872 1.275365856
## [920,] 1.08811604 0.579925761
## [921,] 0.98781685 0.893113803
## [922,] 0.54238893 1.612650122
## [923,] 1.05293723 0.595826634
## [924,] 0.83471490 1.491954192
## [925,] 0.47256689 1.078259600
## [926,] 1.49657778 0.718233202
## [927,] 0.80602665 0.740200203
## [928,] 0.29522740 0.694396450
## [929,] 0.40266870 0.315911390
## [930,] 1.09114637 0.858224900
## [931,] 0.50825096 1.089501831
## [932,] 0.16597464 1.299739114
## [933,] 0.69880328 1.282196119
## [934,] 0.89780478 1.344691195
## [935,] 0.75788250 1.150674192
## [936,] 0.94001244 0.292282395
## [937,] 1.16333149 0.980252516
## [938,] 1.84294772 0.824957238
## [939,] 0.26372511 1.033409427
## [940,] 1.01112672 0.532863059
## [941,] 1.49115404 1.154852125
## [942,] 0.57814286 0.729939942
## [943,] 0.68738150 0.912512192
## [944,] 0.63402394 0.592203205
## [945,] 0.59287468 0.942758912
## [946,] 0.20319084 0.332591393
## [947,] 0.36511823 0.232451157
## [948,] 0.36432500 0.290728993
## [949,] 1.00328199 1.223967961
## [950,] 0.83878346 0.574566600
## [951,] 0.80452490 1.233056641
## [952,] 0.43636043 0.452254133
## [953,] 2.95946160 1.713495305
## [954,] 0.70927671 0.914937445
## [955,] 0.87313051 1.583903839
## [956,] 0.27211123 0.397220475
## [957,] 0.59841670 0.436699228
## [958,] 2.22848411 0.592353639
## [959,] 0.80391666 1.719901680
## [960,] 0.82339076 1.511086453
## [961,] 0.56146826 0.204092097
## [962,] 0.57905695 0.624537542
## [963,] 0.91953409 0.767203783
## [964,] 1.12158030 3.794498035
## [965,] 0.47249768 1.359916098
## [966,] 0.41982676 2.446763641
## [967,] 1.33098810 1.784839617
## [968,] 0.80076476 0.859812457
## [969,] 0.80434215 0.901188487
## [970,] 0.50037988 0.749066362
## [971,] 0.65788204 1.205801096
## [972,] 1.17092973 1.401353931
## [973,] 0.74525667 1.289981407
## [974,] 1.10183139 0.679747295
## [975,] 0.52289264 1.001014066
## [976,] 0.33580336 0.481234523
## [977,] 1.78442880 2.798897738
## [978,] 0.51141648 1.097367186
## [979,] 0.34620161 0.846713273
## [980,] 0.33394149 0.225208232
## [981,] 0.70380781 1.117636144
## [982,] 1.01154058 3.097295097
## [983,] 3.31542666 1.521215716
## [984,] 0.41733718 0.433503284
## [985,] 0.48217266 1.288759019
## [986,] 0.89866837 0.714814256
## [987,] 0.72729241 0.529683915
## [988,] 1.18292344 2.953227145
## [989,] 1.53020639 0.332466765
## [990,] 0.94141883 0.892244690
## [991,] 2.51454734 3.686478225
## [992,] 1.59313983 0.856957474
## [993,] 0.72755285 1.418979712
## [994,] 0.44822243 0.797848194
## [995,] 0.91740605 0.421418312
## [996,] 0.70963245 1.053679065
## [997,] 0.61577238 1.991289185
## [998,] 0.23909102 1.782428051
## [999,] 0.75763838 0.616216765
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
##
## $terms
## pca_data ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist <- vegdist(pca_data, species = "bray")
res <- pcoa(dist)
p1 <- as.data.frame(res$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env,.)
ggplot(p1, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
m02 <- betadisper(dist, env$microsite)
m02##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5063 0.4721
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m02)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 0.7313
## Residuals 20 1.05203 0.052602permutest(m02,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 99 0.74
## Residuals 20 1.05203 0.052602
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.72
## Open 0.73133m02.HSD <- TukeyHSD(m02)
boxplot(m02)
m03 <- betadisper(dist, env$shrub_density)
m03##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4721 0.5171 0.4968 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m03)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 0.08827 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(m03,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 99 0.09 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.76000 0.80000
## 10 0.76968 0.89000
## 11 0.84659 0.89212
## 12
## 13
## 14m03.HSD <- TukeyHSD(m03)
boxplot(m03)
m04 <- betadisper(dist, env$site)
m04##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2828 0.3427 0.4625
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156anova(m04)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 0.3247
## Residuals 19 0.89240 0.046969permutest(m04,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 99 0.37
## Residuals 19 0.89240 0.046969
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.61000 0.20
## Cuyama 0.54695 0.32
## Tecopa 0.19772 0.31784m04.HSD <- TukeyHSD(m04)
boxplot(m04)
### 2023 Data
photo_2023 <- read.csv("observations_2023.csv")
summary(photo_2023)## region site site_code microsite
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.00 Length:192701 Length:192701
## 1st Qu.:1.000 1st Qu.:1.00 Class :character Class :character
## Median :2.000 Median :1.00 Mode :character Mode :character
## Mean :1.771 Mean :1.49
## 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :4.000 Max. :2.00
## year shrub_density rep identified_by
## Min. :2023 Min. : 0.000 Min. : 1 Length:192701
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 48176 Class :character
## Median :2023 Median :10.000 Median : 96351 Mode :character
## Mean :2023 Mean : 6.239 Mean : 96351
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:144526
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:192701 Length:192701 Min. :0.000000 Length:192701
## Class :character Class :character 1st Qu.:0.000000 Class :character
## Mode :character Mode :character Median :0.000000 Mode :character
## Mean :0.005392
## 3rd Qu.:0.000000
## Max. :1.000000
## order family genus species
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:192701 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :2photo_2023 <- photo_2023 %>%
filter(common_name != "Human")
photo_2023 <- photo_2023 %>%
filter(common_name != "Human-Camera Trapper")
photo_2023 <- photo_2023 %>%
filter(common_name != "Domestic Dog")
photo_2023 <- photo_2023 %>%
filter(common_name != "Vehicle")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Insect")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Animal")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Bird")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "No CV Result")
count.hit_2023 <- photo_2023 %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_2023)## animal.hit n
## Min. :0.00 Min. : 546
## 1st Qu.:0.25 1st Qu.: 48325
## Median :0.50 Median : 96104
## Mean :0.50 Mean : 96104
## 3rd Qu.:0.75 3rd Qu.:143883
## Max. :1.00 Max. :191662### 2023 had a 0.28% capture rate### Animal Observations by Site_Code
animals_by_sitecode_2023 <- photo_2023%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode_2023 <- animals_by_sitecode_2023 %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result") ### Animal observations by Site 2023
animals_by_site_2023 <- photo_2023 %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site_2023 <- animals_by_site_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") ### Animal observations by Density
animals_by_density_2023 <- photo_2023 %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density_2023 <- animals_by_density_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")### Total Observations 2023
Total_Observations_2023 <- photo_2023 %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")density_obvs_2023 <- merge(animals_by_density_2023, Total_Observations_2023, all = TRUE)
density_obvs_2023$percent_presence <- density_obvs_2023$captures/density_obvs_2023$total### Percent proportion Figure
plot2 <- ggplot(density_obvs_2023, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot2 + scale_fill_manual(values = c("#009900", "#0066cc"))
m2<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_2023)
anova(m2, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 37 2413.4
## microsite 1 89.8 36 2323.6 <2e-16 ***
## common_name 24 2323.6 12 0.0 <2e-16 ***
## microsite:common_name 12 0.0 0 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e2 <- emmeans(m2, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactions#head(e2)animals_density_2023 <- photo_2023 %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density_2023 <- animals_density_2023 %>% filter(common_name != "Blank")pca_data_2023 <- animals_density_2023 ### Created new df for pcoa data
pca_data_2023 <- pca_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_2023)## [1] 23 26env_2023 <- read.csv("environment_2023.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)## [1] 22 5model01 <- adonis(pca_data_2023 ~ microsite*shrub_density, data = env_2023)## 'adonis' will be deprecated: use 'adonis2' insteadmodel01## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.4511 0.45114 1.6082 0.06930 0.129
## shrub_density 1 0.4482 0.44823 1.5978 0.06885 0.124
## Residuals 20 5.6105 0.28052 0.86184
## Total 22 6.5099 1.00000
##
## $call
## adonis(formula = pca_data_2023 ~ microsite * shrub_density, data = env_2023)
##
## $coefficients
## shrub_density Black-tailed Jackrabbit
## (Intercept) -10.398601 2.8438228
## microsite1 -14.216783 -3.7925408
## shrub_density 2.876923 0.4769231
## microsite1:shrub_density NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.545455e-02 0.12820513
## microsite1 -4.545455e-02 0.12820513
## shrub_density -6.331379e-18 -0.01538462
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) -2.5571096 -0.21794872
## microsite1 -2.6480186 -0.21794872
## shrub_density 0.4923077 0.04615385
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -1.8205128 0.47435897 2.6340326
## microsite1 -1.8205128 0.47435897 1.7249417
## shrub_density 0.3384615 -0.07692308 -0.2615385
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.060606 -2.3391608 4.545455e-02
## microsite1 1.606061 -2.4300699 -4.545455e-02
## shrub_density -0.200000 0.4461538 8.356568e-18
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Heermann's Kangaroo Rat Horned Lark
## (Intercept) 0.03263403 -7.849650 0.38461538
## microsite1 -0.05827506 -15.304196 0.38461538
## shrub_density 0.06153846 3.169231 -0.04615385
## microsite1:shrub_density NA NA NA
## Kit Fox Lizards and Snakes Loggerhead Shrike
## (Intercept) 2.1165501 2.727273e-01 -0.4801865
## microsite1 1.9347319 -2.727273e-01 -0.5710956
## shrub_density -0.3230769 -1.513076e-17 0.1230769
## microsite1:shrub_density NA NA NA
## Mammal Merriam's Kangaroo Rat
## (Intercept) -0.21794872 1.10256410
## microsite1 -0.21794872 0.10256410
## shrub_density 0.04615385 -0.09230769
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel Salinas Pocket Mouse
## (Intercept) 0.3694639 -0.39044289
## microsite1 -0.2668998 -0.48135198
## shrub_density 0.1538462 0.09230769
## microsite1:shrub_density NA NA
## Say's Phoebe Vesper Sparrow Western whiptail
## (Intercept) 0.264568765 1.5384615 0.12820513
## microsite1 -0.008158508 1.5384615 0.12820513
## shrub_density -0.015384615 -0.1846154 -0.01538462
## microsite1:shrub_density NA NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.35547786
## microsite1 -0.09906760
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 1.08713373 1.19242360 1.04021622 0.53368864
## microsite1 0.27702067 0.36812565 0.28650205 -0.08921598
## shrub_density -0.05750503 -0.07418322 -0.03323392 0.02592824
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 1.09169046 0.725232927 0.9149049 0.73036046
## microsite1 0.43517627 -0.003397368 0.1285966 -0.03557956
## shrub_density -0.08696283 -0.012927632 -0.0247195 0.01666357
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.3003752 1.4998669 0.80294716 0.64129703
## microsite1 0.6190745 0.7936762 0.05159731 -0.03324735
## shrub_density -0.1227435 -0.1491194 -0.01154984 0.01898538
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.99239018 0.66809163 0.63858697 -0.08681298
## microsite1 0.34039631 -0.04876636 -0.21003546 -0.87304773
## shrub_density -0.06950611 -0.01035118 0.04254088 0.14042524
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.17811261 0.1694050 -0.01538676 0.91285079
## microsite1 -0.64882951 -0.6891926 -0.81989153 0.22319732
## shrub_density 0.09926822 0.1072032 0.13358216 -0.04137692
## microsite1:shrub_density NA NA NA NA
## 21 22 23
## (Intercept) 0.95345668 1.13091299 0.9134932
## microsite1 0.30456994 0.48161788 0.2756840
## shrub_density -0.05633216 -0.08893335 -0.0522899
## microsite1:shrub_density NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 2.7382748 0.7171803
## [2,] 1.0996783 1.4344806
## [3,] 2.3782813 0.8155126
## [4,] 0.8280660 0.4049624
## [5,] 0.7374631 1.0327982
## [6,] 0.5944951 0.4515525
## [7,] 0.2700776 1.8120483
## [8,] 1.6040864 1.2528508
## [9,] 0.3660925 2.0217544
## [10,] 1.0117398 1.7981395
## [11,] 0.9781887 2.1096450
## [12,] 0.3537379 0.3642225
## [13,] 0.6869765 0.6567368
## [14,] 0.3115720 2.1928635
## [15,] 1.7331453 1.0169350
## [16,] 0.9596079 0.9861569
## [17,] 1.8406550 0.4119059
## [18,] 0.3954707 1.0367186
## [19,] 0.4924088 1.2389746
## [20,] 1.0030954 0.6866150
## [21,] 1.0640408 0.6412791
## [22,] 0.9843796 0.3781761
## [23,] 0.7182460 0.9079104
## [24,] 0.6055968 0.8098724
## [25,] 0.2824401 0.6837748
## [26,] 0.5465991 1.1281813
## [27,] 0.9923677 0.9763268
## [28,] 1.6439075 2.2489744
## [29,] 1.5983089 1.0626882
## [30,] 0.3395985 0.2210830
## [31,] 0.5354982 1.0033802
## [32,] 0.4518611 1.0375600
## [33,] 0.9156338 0.6945022
## [34,] 0.7326711 0.8719917
## [35,] 1.3270950 1.1630793
## [36,] 0.8555655 0.5909281
## [37,] 1.1364610 1.9181392
## [38,] 0.9847799 1.1672615
## [39,] 0.6691929 1.2085231
## [40,] 1.0459579 1.4759968
## [41,] 1.0358562 0.8281517
## [42,] 0.1351022 0.7852149
## [43,] 2.1573028 1.0567682
## [44,] 1.2066674 0.5195105
## [45,] 0.7119275 1.1815594
## [46,] 1.3999410 0.8208377
## [47,] 0.8715805 0.4762804
## [48,] 1.0036517 0.3713635
## [49,] 0.9013351 1.1669415
## [50,] 0.7913610 1.3989553
## [51,] 0.8824895 1.3064191
## [52,] 1.5838122 0.3706992
## [53,] 0.4668202 0.5556006
## [54,] 1.3747239 0.7677864
## [55,] 0.7102356 0.7489866
## [56,] 1.1258842 0.9371586
## [57,] 0.7633517 1.4325719
## [58,] 1.0539526 0.8244041
## [59,] 0.7733155 0.5959915
## [60,] 2.1202396 1.2558250
## [61,] 0.4000565 0.6605348
## [62,] 0.7346400 0.7047121
## [63,] 0.5636768 0.4251710
## [64,] 1.3602660 0.9158437
## [65,] 3.5742708 1.1446878
## [66,] 0.5392069 1.0717975
## [67,] 1.0402897 2.7969678
## [68,] 0.8564135 0.9154163
## [69,] 1.3147426 1.0918524
## [70,] 1.0151126 0.8037166
## [71,] 2.6822804 1.0277958
## [72,] 0.8316963 1.2658315
## [73,] 3.1110483 1.5605846
## [74,] 0.7861471 0.6792935
## [75,] 0.8190844 0.2677384
## [76,] 1.4115869 0.8721571
## [77,] 0.4187621 1.5049565
## [78,] 1.3344876 1.4396763
## [79,] 0.8082365 0.3968630
## [80,] 1.6092280 2.4011541
## [81,] 0.6209134 0.7288840
## [82,] 1.5338524 0.7781797
## [83,] 0.4463297 1.3301870
## [84,] 0.5014001 1.6453936
## [85,] 2.1915007 0.4617612
## [86,] 0.1755958 1.2038608
## [87,] 0.9807270 1.7546097
## [88,] 1.0925057 0.5535764
## [89,] 1.1285489 0.9410264
## [90,] 1.3845553 1.0935328
## [91,] 0.8005270 0.8543149
## [92,] 1.7747441 0.8148711
## [93,] 0.9656347 0.8850079
## [94,] 0.6724260 0.9625644
## [95,] 0.2211525 0.8763503
## [96,] 0.9643899 0.9209384
## [97,] 0.2984458 0.5916444
## [98,] 0.8318393 1.0643771
## [99,] 0.3485737 0.5600867
## [100,] 1.4211614 0.5178163
## [101,] 0.8369026 1.5334397
## [102,] 0.9811977 1.2179178
## [103,] 1.6022900 0.7091963
## [104,] 0.5926428 0.4787760
## [105,] 0.7038819 1.8297473
## [106,] 0.6762639 0.1467004
## [107,] 0.9063436 0.6218488
## [108,] 0.7048776 1.2205993
## [109,] 1.0545086 0.3796934
## [110,] 0.3046786 0.5413877
## [111,] 1.0339587 0.7049948
## [112,] 0.6323795 1.2105481
## [113,] 0.8595198 0.6784251
## [114,] 0.4045428 0.4594704
## [115,] 0.7212456 1.2271784
## [116,] 0.5415000 0.7366891
## [117,] 0.7969268 0.8390085
## [118,] 0.7309664 0.7818723
## [119,] 1.1482505 1.4215844
## [120,] 1.3587255 1.5311380
## [121,] 1.3841010 1.5219691
## [122,] 0.3496842 1.5392874
## [123,] 0.9228000 0.3786625
## [124,] 0.7945953 1.9580517
## [125,] 0.8799227 1.0240600
## [126,] 1.5267668 0.5968688
## [127,] 0.6450349 1.5959289
## [128,] 1.3204904 2.2284481
## [129,] 0.4517017 0.2286479
## [130,] 1.7248174 2.2161323
## [131,] 0.5792634 0.4573465
## [132,] 0.2539590 0.4914602
## [133,] 0.9787231 1.3675160
## [134,] 2.0534593 1.8354910
## [135,] 0.8936060 1.2910296
## [136,] 1.1117847 1.1632545
## [137,] 0.7230107 0.6297134
## [138,] 0.4846413 0.7945592
## [139,] 0.5727237 0.5243089
## [140,] 0.3890955 0.5364520
## [141,] 1.5884495 0.4146156
## [142,] 1.6719877 1.2796764
## [143,] 0.7227712 1.4025224
## [144,] 1.6980909 1.0824444
## [145,] 0.8471497 0.7650705
## [146,] 0.7701617 0.7934832
## [147,] 0.4243351 0.8323766
## [148,] 1.0581203 2.0158779
## [149,] 1.4292932 1.0927163
## [150,] 1.5770383 0.7831433
## [151,] 0.4843029 2.5750417
## [152,] 2.3321275 1.4589910
## [153,] 0.9541153 1.4787789
## [154,] 0.8462835 1.0951974
## [155,] 0.4845349 0.5057734
## [156,] 0.4447651 0.5497398
## [157,] 1.4249147 1.4249616
## [158,] 0.8641810 0.5353499
## [159,] 0.7535915 1.4157121
## [160,] 0.7211604 1.7423315
## [161,] 1.6697859 1.0926917
## [162,] 0.4850397 1.0495755
## [163,] 0.9494819 1.0830195
## [164,] 0.5013202 0.3453050
## [165,] 1.4455322 1.4107392
## [166,] 0.4575864 1.0590453
## [167,] 0.8587519 0.9189470
## [168,] 1.7314059 0.5817640
## [169,] 1.1446649 1.2892957
## [170,] 0.1944590 0.4050381
## [171,] 1.0248612 0.9545152
## [172,] 1.7270129 1.7498332
## [173,] 0.6096415 0.2546216
## [174,] 0.8369686 0.6958042
## [175,] 0.4132246 0.8197026
## [176,] 0.3661891 1.0796383
## [177,] 0.6077763 1.0529853
## [178,] 1.0715529 1.0561281
## [179,] 1.7001175 0.7091150
## [180,] 0.7361720 0.8475822
## [181,] 0.8762563 1.0379011
## [182,] 1.2684685 1.1919251
## [183,] 0.4276396 1.2611834
## [184,] 2.8355078 0.9993001
## [185,] 0.7768380 1.2091902
## [186,] 0.2092950 1.0856551
## [187,] 0.8358542 1.8473285
## [188,] 0.4156293 1.2109307
## [189,] 0.3714976 0.7367634
## [190,] 1.4092837 0.8752986
## [191,] 2.8103818 1.5931489
## [192,] 0.4381570 0.9683500
## [193,] 0.6628551 1.4338904
## [194,] 1.3559238 0.5258778
## [195,] 1.0831014 0.4337764
## [196,] 0.7431218 1.5546510
## [197,] 0.7119704 0.7677798
## [198,] 0.6519377 1.2742883
## [199,] 1.6637430 1.7226449
## [200,] 0.7407142 1.9124531
## [201,] 2.3250093 0.7669119
## [202,] 0.6430732 1.4264210
## [203,] 0.2251508 0.7628526
## [204,] 0.7382890 1.7471426
## [205,] 0.7358557 1.8307957
## [206,] 1.1976856 0.1871336
## [207,] 1.3850117 1.5236655
## [208,] 3.6285246 0.1543495
## [209,] 2.3378367 1.2511940
## [210,] 0.7502639 1.0607068
## [211,] 0.2650657 0.9638570
## [212,] 0.5112553 0.8759559
## [213,] 0.2517901 0.5253464
## [214,] 0.4581573 0.4394306
## [215,] 0.8427951 1.1174081
## [216,] 1.1153856 1.2496810
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## [691,] 1.6368459 0.5922142
## [692,] 0.9199678 1.3040585
## [693,] 1.9356236 0.4282529
## [694,] 0.6056766 0.2658254
## [695,] 1.2626442 0.8848786
## [696,] 0.7682327 0.5175373
## [697,] 0.5825086 0.8098549
## [698,] 1.5933354 0.7877992
## [699,] 0.2719387 0.6760623
## [700,] 0.4128934 1.2284595
## [701,] 1.1414389 0.5265969
## [702,] 0.7433817 0.9231503
## [703,] 1.0971801 0.6835050
## [704,] 0.4767049 0.5936488
## [705,] 0.7569361 0.8945945
## [706,] 0.4282155 1.1404023
## [707,] 1.1455097 0.5262836
## [708,] 0.5582155 1.4052397
## [709,] 0.6680880 1.0939124
## [710,] 0.8419851 1.0648067
## [711,] 0.8756049 0.6588284
## [712,] 1.4855438 0.7858286
## [713,] 0.7952430 0.6860099
## [714,] 0.5025330 0.6946269
## [715,] 1.2321457 0.4262903
## [716,] 0.2619687 1.5484430
## [717,] 1.2201731 1.0484016
## [718,] 2.1205141 0.8132115
## [719,] 0.8514425 0.4393004
## [720,] 1.3000004 1.0212809
## [721,] 0.1225122 0.3543487
## [722,] 0.9681674 1.3429191
## [723,] 0.2463508 0.9524104
## [724,] 0.8208026 0.3447471
## [725,] 0.9683591 0.8327358
## [726,] 0.6633877 1.0330029
## [727,] 0.5692377 0.1299180
## [728,] 0.3754544 0.6661033
## [729,] 2.3392762 0.2893635
## [730,] 1.8306658 1.5224859
## [731,] 1.1100527 2.6259266
## [732,] 0.1557883 1.0154624
## [733,] 3.7232258 0.5923218
## [734,] 3.0063114 0.9045910
## [735,] 1.0610028 1.2432407
## [736,] 0.3983563 0.8284331
## [737,] 0.8586571 0.2284890
## [738,] 0.9638408 0.9854984
## [739,] 1.3245368 0.5772708
## [740,] 2.3741223 0.9075456
## [741,] 0.3098802 0.5827357
## [742,] 0.3078606 0.8773913
## [743,] 1.0113884 1.4884377
## [744,] 1.1828203 1.7302464
## [745,] 1.4581638 1.1030047
## [746,] 0.8684319 1.1516594
## [747,] 1.0032254 0.6669102
## [748,] 0.9772989 1.1749810
## [749,] 0.6738043 0.4676554
## [750,] 0.1787842 0.5501696
## [751,] 1.0044021 0.4933203
## [752,] 0.6656481 0.6216719
## [753,] 0.4623568 1.1749961
## [754,] 0.8352325 0.7693856
## [755,] 0.4201885 1.4117215
## [756,] 1.0449122 0.7835850
## [757,] 0.6253478 0.9113521
## [758,] 0.8110692 1.9625250
## [759,] 0.2881151 0.9992459
## [760,] 0.7227180 0.8364296
## [761,] 0.2515428 1.5086606
## [762,] 0.8005548 1.0997582
## [763,] 0.2021728 0.9759520
## [764,] 0.8803604 0.7169446
## [765,] 0.6033294 1.0744713
## [766,] 0.5695719 0.5380103
## [767,] 2.1844210 1.2882888
## [768,] 1.3109725 0.8343514
## [769,] 0.7734289 1.1705051
## [770,] 1.2772370 1.7982242
## [771,] 0.8510570 1.4341436
## [772,] 0.2730946 1.4520035
## [773,] 0.5088346 2.1815348
## [774,] 0.8962447 1.0425626
## [775,] 1.1506337 0.8851086
## [776,] 1.0556097 0.7612639
## [777,] 1.3099935 1.9880015
## [778,] 0.6541766 1.3677700
## [779,] 1.0367594 0.7206970
## [780,] 1.1401624 0.5296758
## [781,] 1.3727543 2.6433162
## [782,] 0.7209186 1.2824811
## [783,] 1.2370039 0.5652595
## [784,] 1.3016810 1.0655017
## [785,] 1.0651528 0.9900332
## [786,] 0.9192623 1.0846283
## [787,] 2.7316337 0.8888452
## [788,] 1.2356377 0.6072537
## [789,] 0.8429879 1.7774124
## [790,] 0.6792695 0.7237951
## [791,] 0.5137883 1.0117059
## [792,] 0.6975670 0.5835361
## [793,] 0.6959790 0.3623321
## [794,] 0.9974067 0.7849763
## [795,] 1.0444049 0.9014131
## [796,] 0.3951821 1.6046673
## [797,] 0.4800807 0.7236967
## [798,] 0.6743464 1.7826001
## [799,] 1.6446054 0.4519389
## [800,] 1.5838862 1.2844503
## [801,] 1.7409729 0.5148704
## [802,] 0.4461590 0.6117800
## [803,] 0.6893886 0.4595770
## [804,] 1.2506738 1.9013684
## [805,] 2.0517455 2.0358691
## [806,] 0.6228277 0.4565032
## [807,] 0.9259661 1.2399171
## [808,] 1.9627551 1.2840617
## [809,] 0.6537201 1.0257056
## [810,] 0.6513838 0.6273337
## [811,] 0.7915049 0.3057482
## [812,] 0.3961761 1.2712514
## [813,] 0.6472418 1.5030755
## [814,] 2.2888326 1.2461410
## [815,] 0.4725135 1.4036298
## [816,] 0.5357719 0.5807133
## [817,] 1.5824029 2.2088563
## [818,] 0.6255094 1.5868429
## [819,] 0.7765597 1.0682722
## [820,] 0.5841069 0.8499209
## [821,] 0.3585005 1.0925057
## [822,] 1.1378691 0.4505460
## [823,] 1.6453440 0.5690062
## [824,] 0.3341070 1.7304638
## [825,] 0.2129236 0.8903641
## [826,] 0.7305571 0.8944324
## [827,] 0.2592391 1.8510019
## [828,] 0.7313600 1.3096716
## [829,] 1.2225318 0.6868632
## [830,] 1.0805237 1.2376928
## [831,] 0.6861552 0.7704690
## [832,] 1.0310521 1.1217127
## [833,] 0.7229002 0.6008686
## [834,] 0.8594878 0.5090439
## [835,] 0.6456431 0.5389815
## [836,] 1.0497801 0.9056697
## [837,] 0.7743263 0.5823938
## [838,] 0.2775914 0.6600846
## [839,] 1.0734097 1.0981067
## [840,] 1.3724453 0.6762862
## [841,] 0.6200563 1.4369126
## [842,] 0.4221652 0.7497118
## [843,] 0.1887104 0.4511190
## [844,] 0.8905813 1.0713214
## [845,] 0.4492508 0.9611427
## [846,] 1.2266377 0.9239308
## [847,] 1.8357903 0.6139745
## [848,] 2.1170610 1.5297623
## [849,] 0.8641904 0.9048875
## [850,] 0.7763564 0.8261014
## [851,] 0.5886437 0.9219957
## [852,] 0.7780805 1.2198779
## [853,] 0.3130145 1.0273264
## [854,] 1.3037881 1.7243142
## [855,] 0.6600778 0.1135421
## [856,] 0.3405642 1.2357915
## [857,] 1.6048183 1.0667869
## [858,] 0.7850148 0.9659300
## [859,] 1.4330423 0.7837892
## [860,] 1.9417944 1.5471746
## [861,] 0.7368109 0.9799005
## [862,] 0.4443651 1.4897043
## [863,] 0.8096961 1.5027805
## [864,] 0.3342372 0.5853036
## [865,] 0.6936222 1.0112454
## [866,] 0.7970887 1.7889110
## [867,] 0.9834582 0.7641085
## [868,] 0.7640428 0.5700905
## [869,] 1.6414408 0.5670252
## [870,] 0.9302196 1.7945324
## [871,] 0.8533056 0.4033542
## [872,] 0.2482495 1.1677066
## [873,] 0.7998040 0.8428989
## [874,] 0.8520182 0.3752923
## [875,] 0.6009293 1.3048680
## [876,] 0.3443136 0.9950623
## [877,] 2.2497036 0.6300975
## [878,] 0.7127964 1.6250996
## [879,] 0.9553326 1.3863370
## [880,] 0.3400701 1.0058723
## [881,] 0.8357676 0.4586675
## [882,] 0.6962055 0.2049235
## [883,] 1.0528344 0.9543984
## [884,] 0.6551738 1.5340469
## [885,] 1.9641078 1.6183076
## [886,] 1.0440074 0.5125522
## [887,] 0.8646239 0.5517355
## [888,] 0.6761364 2.8340474
## [889,] 0.7399453 1.2953866
## [890,] 1.4787130 1.0693455
## [891,] 1.1775275 2.5238715
## [892,] 2.1987639 0.7572682
## [893,] 1.7449447 1.4987778
## [894,] 0.2214034 1.1560529
## [895,] 1.9745993 0.4945942
## [896,] 1.5273327 0.4593395
## [897,] 1.1017223 0.4914373
## [898,] 0.6719017 1.1670288
## [899,] 0.3934572 0.5766324
## [900,] 0.3379552 2.1638225
## [901,] 0.4180118 1.4133276
## [902,] 0.5052528 0.2869777
## [903,] 1.1425264 0.5547063
## [904,] 4.2820413 1.1374841
## [905,] 0.6944172 0.5522295
## [906,] 1.0518811 0.9191484
## [907,] 1.1865574 0.9241486
## [908,] 1.4206617 0.3614167
## [909,] 0.5846067 0.6777883
## [910,] 0.8763929 1.5577614
## [911,] 0.7876491 1.1484446
## [912,] 0.5163299 0.3901655
## [913,] 0.8104888 2.4508215
## [914,] 0.7794721 0.5401396
## [915,] 1.2704495 0.3891202
## [916,] 1.9685345 1.7703449
## [917,] 0.7167361 0.4780141
## [918,] 1.0570261 0.9129683
## [919,] 0.5217457 1.0365086
## [920,] 1.6125546 1.2231105
## [921,] 0.7641024 0.6249500
## [922,] 0.5860518 0.9734472
## [923,] 0.9238994 1.2624703
## [924,] 1.8888542 1.5093125
## [925,] 2.3520741 0.5955637
## [926,] 1.1756421 2.0979918
## [927,] 2.3694816 1.2120009
## [928,] 1.2284399 0.2796685
## [929,] 0.6708727 0.7965035
## [930,] 1.3412596 0.5425654
## [931,] 0.6410777 1.1444281
## [932,] 0.9475407 0.3676355
## [933,] 2.0426579 1.8595329
## [934,] 1.7386576 0.5958084
## [935,] 1.1496454 0.2358684
## [936,] 1.7468585 1.9488821
## [937,] 1.9264065 1.3434620
## [938,] 0.7164047 0.6779951
## [939,] 1.2738789 0.4462101
## [940,] 1.7310872 2.1168610
## [941,] 1.3538728 1.5235136
## [942,] 1.5599813 0.9691400
## [943,] 1.3243285 0.9422121
## [944,] 0.8639356 0.3694354
## [945,] 0.7146255 0.6606062
## [946,] 1.0115684 0.7496037
## [947,] 0.9213956 0.4255782
## [948,] 1.1378723 0.8560517
## [949,] 1.3275265 2.1810385
## [950,] 1.1477997 1.0120646
## [951,] 0.6967618 0.6100558
## [952,] 0.5578072 0.6216000
## [953,] 0.7405553 0.3102498
## [954,] 0.5705637 0.5106031
## [955,] 0.5142820 0.7053741
## [956,] 0.7047356 0.4248170
## [957,] 1.1754134 1.2774289
## [958,] 0.3332258 0.7335940
## [959,] 0.7120403 1.2160229
## [960,] 1.5087946 0.2823302
## [961,] 0.4903416 1.0958285
## [962,] 0.5004908 1.0891064
## [963,] 0.9389418 0.4255239
## [964,] 0.6168014 0.7755318
## [965,] 1.3034819 2.3649265
## [966,] 0.4487397 1.1360021
## [967,] 1.2674244 1.0664486
## [968,] 1.5720549 0.6919854
## [969,] 1.4310354 0.4513660
## [970,] 1.2368114 0.8792416
## [971,] 0.4804327 2.2200502
## [972,] 0.4186984 1.2531901
## [973,] 0.1218204 1.6404477
## [974,] 0.4654093 0.8988662
## [975,] 1.0986846 0.8368617
## [976,] 0.5698935 0.9670671
## [977,] 0.7725930 0.5185224
## [978,] 0.4062727 0.8478946
## [979,] 1.2007296 0.8209012
## [980,] 0.6305821 1.3164336
## [981,] 1.7051376 1.6385660
## [982,] 0.9498648 2.5324336
## [983,] 1.1589795 0.3622011
## [984,] 1.0845641 1.6226098
## [985,] 3.7830547 0.3845841
## [986,] 0.8440801 1.3649315
## [987,] 1.0566599 1.1398269
## [988,] 0.3845612 0.9261189
## [989,] 1.4414629 0.6286629
## [990,] 0.4720613 1.1402347
## [991,] 1.1868236 0.9437842
## [992,] 1.5059606 0.8569690
## [993,] 1.1354860 0.8517288
## [994,] 0.9749544 0.8880004
## [995,] 0.4320570 1.4335156
## [996,] 0.9551517 0.3304445
## [997,] 0.9104851 0.7420362
## [998,] 0.6270105 0.4702040
## [999,] 1.1531363 1.4686492
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 1 10
## 17 1 1 11
## 18 1 1 11
## 19 1 1 10
## 20 1 -1 0
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
##
## $terms
## pca_data_2023 ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_2023, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_2023 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_2023 <- vegdist(pca_data_2023, species = "bray")
res_2023 <- pcoa(dist_2023)
p2 <- as.data.frame(res_2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_2023,.)
ggplot(p2, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
model02 <- betadisper(dist_2023, env_2023$microsite)
model02##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Density Open
## 0.4638 0.5348
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model02)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 0.2301
## Residuals 21 0.39782 0.018944permutest(model02,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 99 0.22
## Residuals 21 0.39782 0.018944
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.26
## Open 0.23013model02.HSD <- TukeyHSD(model02)
boxplot(model02)
model03 <- betadisper(dist_2023, env_2023$shrub_density)
model03##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.5348 0.3051 0.4393 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model03)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.71375 0.142751 4.86 0.006083 **
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model03,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.71375 0.142751 4.86 99 0.02 *
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.030000 0.150000
## 10 0.059818 0.380000
## 11 0.192393 0.435177
## 12
## 13
## 14model03.HSD <- TukeyHSD(model03)
boxplot(model03)
model04 <- betadisper(dist_2023, env_2023$site)
model04##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.5093 0.4594 0.4488
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835anova(model04)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 0.6392
## Residuals 20 0.36181 0.0180906permutest(model04,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 99 0.71
## Residuals 20 0.36181 0.0180906
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.66000 0.14
## Cuyama 0.56406 0.92
## Tecopa 0.14036 0.89377model04.HSD <- TukeyHSD(model04)
boxplot(model04)
### From running the community compositions both in 2022 and 2023 it
seems the community compositions across densities, microsites, and sites
are all somewhat similar with no significant differences.
Run PCOAs for both years combined!
photo_final <- read.csv("observations_final.csv")
summary(photo_final)## region site site_code microsite
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.000 Length:250716 Length:250716
## 1st Qu.:1.000 1st Qu.:1.000 Class :character Class :character
## Median :2.000 Median :1.000 Mode :character Mode :character
## Mean :1.772 Mean :1.498
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :2.000
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:250716
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 21744 Class :character
## Median :2023 Median :10.000 Median : 67344 Mode :character
## Mean :2023 Mean : 5.878 Mean : 77566
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:130022
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:250716 Length:250716 Min. :0.00000 Length:250716
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.01923
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:250716 Min. : 1
## Class :character 1st Qu.: 1
## Mode :character Median : 1
## Mean : 1
## 3rd Qu.: 1
## Max. :12photo_final <- photo_final %>%
filter(common_name != "Human")
photo_final <- photo_final %>%
filter(common_name != "Human-Camera Trapper")
photo_final <- photo_final %>%
filter(common_name != "Domestic Dog")
photo_final <- photo_final %>%
filter(common_name != "Vehicle")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Insect")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Animal")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Bird")
count.hit_final <- photo_final %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_final)## animal.hit n
## Min. :0.00 Min. : 3717
## 1st Qu.:0.25 1st Qu.: 64261
## Median :0.50 Median :124806
## Mean :0.50 Mean :124806
## 3rd Qu.:0.75 3rd Qu.:185350
## Max. :1.00 Max. :245894# Wow we have a 1.489% capture rate for the project!### Animal Observations by Site_Code
animals_by_sitecode_final <- photo_final%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.animals_by_sitecode_final <- animals_by_sitecode_final %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result") animals_by_site_final <- photo_final %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.animals_by_site_final <- animals_by_site_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")animals_by_density_final<- photo_final %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.animals_by_density_final <- animals_by_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")Total_Observations_final <- photo_final %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") %>% filter(common_name != "Mammal")density_obvs_final <- merge(animals_by_density_final, Total_Observations_final, all = TRUE) %>% filter(common_name != "Mammal")
density_obvs_final$percent_presence <- density_obvs_final$captures/density_obvs_final$totalm3<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_final)
anova(m3, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## common_name 32 27791.1 22 0 < 2.2e-16 ***
## microsite:common_name 22 0.0 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e3 <- emmeans(m3, pairwise~common_name)## NOTE: Results may be misleading due to involvement in interactions#head(e3)animals_density_final <- photo_final %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.animals_density_final <- animals_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "Mammal")pca_data_final <- animals_density_final ### Created new df for pca data
pca_data_final <- pca_data_final %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_final)## [1] 24 35env_final <- read.csv("environment_final.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env_final)## [1] 24 5model010 <- adonis(pca_data_final ~ microsite*shrub_density, data = env_final)## 'adonis' will be deprecated: use 'adonis2' insteadmodel010## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2680 0.26799 1.0165 0.04244 0.350
## shrub_density 1 0.5102 0.51024 1.9354 0.08081 0.093 .
## Residuals 21 5.5363 0.26363 0.87675
## Total 23 6.3145 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = pca_data_final ~ microsite * shrub_density,
## data = env_final)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -10.557692 4.166667e-02 -11.346154
## microsite1 -14.057692 -4.166667e-02 -22.012821
## shrub_density 2.876923 9.476236e-19 4.661538
## microsite1:shrub_density NA NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.166667e-02 1.1185897
## microsite1 -4.166667e-02 1.0352564
## shrub_density 4.564605e-18 -0.1692308
## microsite1:shrub_density NA NA
## Bobcat Brewer's Blackbird
## (Intercept) -0.7019231 -2.4070513
## microsite1 -1.1185897 -2.8237179
## shrub_density 0.1692308 0.5538462
## microsite1:shrub_density NA NA
## California Ground Squirrel California Pocket Mouse
## (Intercept) 19.618590 0.33974359
## microsite1 13.868590 0.17307692
## shrub_density -1.769231 -0.03076923
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -6.185897 -1.0 2.5961538
## microsite1 -7.352564 -1.0 -1.9038462
## shrub_density 1.292308 0.2 0.1384615
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.2467949 -27.230769 1.958333e+00
## microsite1 -2.8365385 -28.897436 -1.958333e+00
## shrub_density 0.4153846 5.307692 3.783904e-16
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Great White Egret
## (Intercept) 6.8141026 4.166667e-02
## microsite1 4.6474359 -4.166667e-02
## shrub_density -0.7076923 4.564605e-18
## microsite1:shrub_density NA NA
## Greater Roadrunner Heermann's Kangaroo Rat Horned Lark
## (Intercept) -4.3814103 125.403846 0.38461538
## microsite1 -4.4647436 50.903846 0.38461538
## shrub_density 0.8307692 -4.538462 -0.04615385
## microsite1:shrub_density NA NA NA
## Killdeer Kit Fox Lark Sparrow
## (Intercept) 4.166667e-02 2.5288462 -1.8717949
## microsite1 -4.166667e-02 2.1121795 -2.2051282
## shrub_density 4.564605e-18 -0.3384615 0.3846154
## microsite1:shrub_density NA NA NA
## Loggerhead Shrike Merriam's Kangaroo Rat
## (Intercept) -1.1025641 1.06089744
## microsite1 -1.4358974 0.14423077
## shrub_density 0.2923077 -0.09230769
## microsite1:shrub_density NA NA
## Mohave Ground Squirrel Mourning Dove
## (Intercept) 2.916667e-01 -1.9551282
## microsite1 -2.916667e-01 -2.1217949
## shrub_density -1.570024e-16 0.3846154
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel No CV Result
## (Intercept) 18.894231 -0.8685897
## microsite1 13.977564 -0.9519231
## shrub_density -2.092308 0.1692308
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Say's Phoebe
## (Intercept) -0.5641026 -0.35256410 0.253205128
## microsite1 -0.5641026 -0.51923077 0.003205128
## shrub_density 0.1076923 0.09230769 -0.015384615
## microsite1:shrub_density NA NA NA
## Vesper Sparrow Western whiptail
## (Intercept) 1.5801282 0.12820513
## microsite1 1.4967949 0.12820513
## shrub_density -0.1846154 -0.01538462
## microsite1:shrub_density NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.33653846
## microsite1 -0.08012821
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.78080317 0.82434750 0.98306093 0.76605527
## microsite1 0.17504154 0.26134577 0.28849450 0.24337903
## shrub_density -0.03080745 -0.04609079 -0.04138812 -0.03373393
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.84838652 0.83124969 0.86997845 0.76272593
## microsite1 0.30313950 0.28542834 0.29904628 0.20499025
## shrub_density -0.05068255 -0.04597727 -0.04282439 -0.02976002
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7481015 1.5203179 1.00167997 1.0125329
## microsite1 1.0946409 0.9430041 0.41975528 0.4948288
## shrub_density -0.1923781 -0.1650103 -0.07142752 -0.0794049
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.88500919 0.84806327 0.8076157 0.83755826
## microsite1 0.18017241 0.07715532 0.2071152 0.27863228
## shrub_density -0.03799694 -0.02089895 -0.0336203 -0.04242666
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.04881892 0.31671402 0.25149293 0.07189331
## microsite1 -0.82473427 -0.56994858 -0.63539881 -0.82608604
## shrub_density 0.13158888 0.08613734 0.09735222 0.13310018
## microsite1:shrub_density NA NA NA NA
## 21 22 23 24
## (Intercept) 1.12355908 1.11712532 1.06379973 0.830255162
## microsite1 0.41343026 0.46257310 0.31720419 0.079719825
## shrub_density -0.06585209 -0.07418334 -0.04509589 -0.006831989
## microsite1:shrub_density NA NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.23496499 0.76822600
## [2,] 0.09262485 0.78962868
## [3,] 1.18384241 1.26968747
## [4,] 0.57125392 0.31991878
## [5,] 0.87128256 0.12714980
## [6,] 0.35392365 0.57198682
## [7,] 2.17368995 1.35872842
## [8,] 2.08285104 1.13426979
## [9,] 1.36433165 1.13533064
## [10,] 0.87152326 1.27718874
## [11,] 1.80510162 1.20285210
## [12,] 1.22711455 0.77486169
## [13,] 0.55485593 0.60529275
## [14,] 0.51329958 1.08130429
## [15,] 0.81333146 0.92130702
## [16,] 0.21106459 0.75207613
## [17,] 0.62200224 1.75191490
## [18,] 1.31580411 0.77065101
## [19,] 1.30913225 1.77719594
## [20,] 0.29381370 0.79190199
## [21,] 0.92878270 0.82689638
## [22,] 0.65787974 1.15645742
## [23,] 1.35407052 1.72986031
## [24,] 1.16148447 0.71405687
## [25,] 0.61306997 1.25917298
## [26,] 3.04091314 1.43482159
## [27,] 1.19171173 0.47147925
## [28,] 0.96115296 2.29478556
## [29,] 0.60245941 0.82175516
## [30,] 1.17128084 1.08757781
## [31,] 0.40921525 0.16592538
## [32,] 0.42484241 0.65679364
## [33,] 1.32529812 0.84093876
## [34,] 1.20355413 0.57478006
## [35,] 0.47409043 1.43136717
## [36,] 0.40839782 1.02353742
## [37,] 1.78289211 0.62306854
## [38,] 0.27561241 0.45059042
## [39,] 0.61379139 0.50412058
## [40,] 0.71339354 0.04957232
## [41,] 0.64191262 1.14814601
## [42,] 1.06574934 1.83195204
## [43,] 0.47305735 1.27191887
## [44,] 0.81681129 0.99825885
## [45,] 1.84026590 0.60303259
## [46,] 0.36552672 1.04491054
## [47,] 0.34691104 1.13974393
## [48,] 0.69445817 0.67169563
## [49,] 0.57180786 0.60790895
## [50,] 1.08892673 1.13312879
## [51,] 0.39566204 1.22019889
## [52,] 0.40118071 0.66226615
## [53,] 0.96721055 1.01216833
## [54,] 2.72519774 0.54672642
## [55,] 0.90114879 1.25828231
## [56,] 0.45828575 0.77411083
## [57,] 0.38296044 0.99896338
## [58,] 2.63183224 0.41259748
## [59,] 1.10815434 1.33958340
## [60,] 1.14829028 0.59348135
## [61,] 3.16349145 0.30148756
## [62,] 0.49226254 0.76437714
## [63,] 0.47147549 0.62199387
## [64,] 1.24956567 0.60280292
## [65,] 2.55197016 1.34633592
## [66,] 0.77672126 0.39741696
## [67,] 1.05819297 0.51260812
## [68,] 0.51481016 1.38842748
## [69,] 0.48936077 0.82768205
## [70,] 1.31595954 0.84850599
## [71,] 0.54312278 0.45900817
## [72,] 1.56946007 1.18525528
## [73,] 1.03203900 0.95503071
## [74,] 1.50534580 2.00793214
## [75,] 0.78320188 1.24361365
## [76,] 0.43910399 0.53117859
## [77,] 0.85296329 2.55225247
## [78,] 0.54418543 1.05499846
## [79,] 0.30065605 0.55230929
## [80,] 0.81400978 1.05495652
## [81,] 1.57200386 1.20741046
## [82,] 2.01193873 1.06802623
## [83,] 1.23898562 1.60966910
## [84,] 0.34608247 1.32901461
## [85,] 0.60540667 1.68122000
## [86,] 0.70090906 0.23643450
## [87,] 0.37018345 1.38374964
## [88,] 0.27402380 0.21760893
## [89,] 0.97201067 0.76773463
## [90,] 0.36969817 0.63978732
## [91,] 0.44285040 0.37010608
## [92,] 0.68820415 1.28244589
## [93,] 1.32181129 1.47178016
## [94,] 0.70340487 1.85184083
## [95,] 1.22137542 1.15580348
## [96,] 1.07909015 1.18784623
## [97,] 2.59015383 0.43232040
## [98,] 0.35477374 0.89806208
## [99,] 0.54387297 0.25645687
## [100,] 1.02867024 0.71884632
## [101,] 0.70688312 1.21693517
## [102,] 0.96991024 0.33943583
## [103,] 0.67086163 0.56789582
## [104,] 0.99954014 1.52061233
## [105,] 2.86087470 0.51774225
## [106,] 0.78329801 0.26357793
## [107,] 1.95279825 1.28736227
## [108,] 2.52627037 1.07544489
## [109,] 0.75917183 0.96515201
## [110,] 1.47933680 0.76925569
## [111,] 1.07448800 0.83606625
## [112,] 0.61778563 0.86103001
## [113,] 1.01175732 1.05922976
## [114,] 1.04001402 0.80499863
## [115,] 0.36784659 2.18958840
## [116,] 0.35349119 0.47400929
## [117,] 0.92024277 3.02365204
## [118,] 0.49843473 1.29407481
## [119,] 0.16519736 1.02703752
## [120,] 3.60215479 4.09922213
## [121,] 0.64748969 0.84909117
## [122,] 0.97227726 0.46225445
## [123,] 0.85779803 1.37842307
## [124,] 0.41284416 1.05877495
## [125,] 1.43512238 0.90422665
## [126,] 0.63549206 0.63588864
## [127,] 2.25620928 1.00807733
## [128,] 0.68819317 1.09436543
## [129,] 0.66003448 1.26129678
## [130,] 0.55465950 0.34856940
## [131,] 0.67452428 1.08348688
## [132,] 0.37027455 1.40899783
## [133,] 0.84616144 1.76434489
## [134,] 0.96254284 0.84543650
## [135,] 0.94278679 0.42203839
## [136,] 0.61415909 1.09665447
## [137,] 0.72564851 1.02409447
## [138,] 1.81477909 0.57145896
## [139,] 0.30017708 1.00338418
## [140,] 0.76325194 0.95073779
## [141,] 1.74536118 0.19128239
## [142,] 2.38025595 0.53772008
## [143,] 0.58619960 0.39404703
## [144,] 1.08766112 0.57801442
## [145,] 1.19151084 1.68009279
## [146,] 1.81772224 0.26932175
## [147,] 0.57721586 0.54115892
## [148,] 1.44656976 0.35487447
## [149,] 0.32281866 0.34805550
## [150,] 1.13899089 0.77161896
## [151,] 0.90746361 0.72461985
## [152,] 1.27903982 1.69308437
## [153,] 0.38856981 0.19946582
## [154,] 0.71491349 0.37221828
## [155,] 0.83061118 0.30289671
## [156,] 0.57332442 0.46858393
## [157,] 2.13079914 0.77731826
## [158,] 1.82279217 0.38564811
## [159,] 0.81474769 1.28368930
## [160,] 0.66715899 0.25442281
## [161,] 1.50063894 0.69963219
## [162,] 2.69875437 0.75400009
## [163,] 1.94894501 3.03488354
## [164,] 0.26210100 0.98694408
## [165,] 0.57075177 0.60806065
## [166,] 0.37860271 1.37008547
## [167,] 1.11665152 1.14622578
## [168,] 0.59338012 0.54393255
## [169,] 0.82346503 0.74708163
## [170,] 0.98027952 1.43347113
## [171,] 0.67424219 1.08330196
## [172,] 0.20376826 0.81721116
## [173,] 0.53075044 0.48261081
## [174,] 0.84099286 0.67146963
## [175,] 0.77816047 1.07351881
## [176,] 0.49296191 2.23734317
## [177,] 1.45006564 1.16440119
## [178,] 0.79602786 0.61307255
## [179,] 0.44377160 0.63529680
## [180,] 0.74583253 1.13846282
## [181,] 1.87256788 0.89777274
## [182,] 0.55513451 0.66704391
## [183,] 0.79187215 0.59276757
## [184,] 0.52393202 0.97500224
## [185,] 1.03140071 0.56333618
## [186,] 1.08411182 0.62303549
## [187,] 0.47003035 0.33991961
## [188,] 0.66233133 0.84642238
## [189,] 1.25907307 0.92713518
## [190,] 1.58721954 1.84937902
## [191,] 1.65703053 0.50157984
## [192,] 1.69792511 1.32153299
## [193,] 1.12405265 0.75721310
## [194,] 0.15001982 0.90113233
## [195,] 0.95859902 1.15299575
## [196,] 0.62385738 1.55647744
## [197,] 2.32738051 1.18106587
## [198,] 0.94397100 0.92355499
## [199,] 1.42815390 0.59202929
## [200,] 1.07149797 1.08039971
## [201,] 0.30295843 0.22647207
## [202,] 0.92065095 0.22075479
## [203,] 0.60398728 0.59814691
## [204,] 1.00086191 1.25091170
## [205,] 0.90859917 0.42034767
## [206,] 0.55287135 1.12024521
## [207,] 1.04847958 0.39442770
## [208,] 1.04345929 1.99764923
## [209,] 3.86803730 2.13959800
## [210,] 0.36993936 2.38978789
## [211,] 0.17238469 0.79280446
## [212,] 1.01591937 0.94828480
## [213,] 1.88663580 0.65076264
## [214,] 1.01205722 0.97782396
## [215,] 0.95497428 0.14763276
## [216,] 1.01802204 0.73677650
## [217,] 0.65711066 2.46777280
## [218,] 1.10046370 1.23546395
## [219,] 0.51448353 2.07089544
## [220,] 0.54101344 1.48541975
## [221,] 1.44514787 1.50623721
## [222,] 0.82863072 1.08739610
## [223,] 0.79319000 1.48236777
## [224,] 3.50382285 0.10063593
## [225,] 1.00023433 2.01904878
## [226,] 0.83598186 0.92834611
## [227,] 1.81025856 1.41169606
## [228,] 0.62484540 1.49006575
## [229,] 0.30484097 1.14592517
## [230,] 0.90203826 0.66393283
## [231,] 0.51609709 0.59620228
## [232,] 1.56121546 2.01887089
## [233,] 2.47703657 3.54717428
## [234,] 0.82901295 0.93159165
## [235,] 1.02293334 0.23793669
## [236,] 0.35125761 1.30311031
## [237,] 0.69746460 0.84413677
## [238,] 1.70146701 1.68056892
## [239,] 0.55414092 0.44755628
## [240,] 0.62833012 1.45677859
## [241,] 0.37729532 0.77959687
## [242,] 0.60634140 0.32452251
## [243,] 0.62777299 0.24791233
## [244,] 2.11441142 0.76470928
## [245,] 1.90639377 1.39181232
## [246,] 0.48480877 0.40252303
## [247,] 0.32313667 0.34969310
## [248,] 0.20460734 0.40942160
## [249,] 2.43049778 0.46401759
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## [704,] 0.32267376 0.55315977
## [705,] 1.71697070 0.50234691
## [706,] 1.42427196 0.59650605
## [707,] 1.01681958 0.65872191
## [708,] 0.55341236 2.62275562
## [709,] 1.38433121 1.28155355
## [710,] 0.46726267 0.37181688
## [711,] 0.43250038 0.85676962
## [712,] 0.54244318 1.26185221
## [713,] 0.65482474 0.98966029
## [714,] 0.80213455 1.20133293
## [715,] 0.60410980 1.36363528
## [716,] 0.60929269 2.74517755
## [717,] 0.65998731 0.83682773
## [718,] 0.76203787 0.55455904
## [719,] 0.43005870 2.27324622
## [720,] 1.47500320 2.61622652
## [721,] 1.48985505 1.56574167
## [722,] 1.48218712 1.30734662
## [723,] 0.53804585 1.96204754
## [724,] 1.74866266 4.64455458
## [725,] 1.21054205 0.40177484
## [726,] 1.96617420 0.77683058
## [727,] 0.31311542 0.72744547
## [728,] 0.30945494 0.58905209
## [729,] 0.58203729 1.14435408
## [730,] 0.35677416 0.86376471
## [731,] 0.35443035 0.42842719
## [732,] 0.64878701 1.27751249
## [733,] 0.53091687 1.43948616
## [734,] 0.85034167 1.32671994
## [735,] 0.39863345 0.60843373
## [736,] 0.81170410 1.36827245
## [737,] 0.67009454 0.69287715
## [738,] 0.42731974 0.63331259
## [739,] 1.32245967 1.91043195
## [740,] 0.51628874 1.50069308
## [741,] 0.71002896 2.17552440
## [742,] 1.28789491 0.50262546
## [743,] 0.58077105 1.80107869
## [744,] 0.73495813 0.92168058
## [745,] 0.88998802 0.53295886
## [746,] 0.34928720 0.85904279
## [747,] 0.28059664 0.66313503
## [748,] 0.80969302 0.48213325
## [749,] 1.01776289 0.85409912
## [750,] 0.85740671 0.99809637
## [751,] 0.73489000 0.44938612
## [752,] 0.92373100 0.42111202
## [753,] 1.47178927 0.79670651
## [754,] 0.82943494 1.18176641
## [755,] 0.71080895 0.34088550
## [756,] 1.13784285 1.33457515
## [757,] 0.83571188 1.10493060
## [758,] 1.57958734 0.78321726
## [759,] 0.42345294 0.92130321
## [760,] 0.62940449 0.72548098
## [761,] 1.30814866 2.33205705
## [762,] 0.77454054 1.10292536
## [763,] 1.40756403 0.99642037
## [764,] 1.45325955 1.53280667
## [765,] 1.01501318 2.77405336
## [766,] 1.06762095 1.38065706
## [767,] 0.52819477 0.57855194
## [768,] 0.60428598 0.50227216
## [769,] 0.80023874 1.44258724
## [770,] 0.35461246 0.54786371
## [771,] 0.56235950 1.52272423
## [772,] 0.47816371 0.61243980
## [773,] 1.55810647 1.14383038
## [774,] 1.11052846 1.02813544
## [775,] 0.66649858 0.28801534
## [776,] 0.47402627 0.91131822
## [777,] 1.23882068 1.15409747
## [778,] 1.45707330 1.33660120
## [779,] 1.90279063 1.33280970
## [780,] 1.54804185 0.59218224
## [781,] 1.10098209 1.76421618
## [782,] 0.50706905 2.91422902
## [783,] 0.71496634 0.22146741
## [784,] 0.98307332 2.50810954
## [785,] 1.09457465 1.17186721
## [786,] 0.61594502 1.08426061
## [787,] 0.93406629 1.00113049
## [788,] 0.84253889 0.22527246
## [789,] 1.55665835 2.70224164
## [790,] 1.44649807 1.90201942
## [791,] 1.11619320 1.06751457
## [792,] 0.09383895 0.61235674
## [793,] 1.47135751 0.60098051
## [794,] 1.58005465 0.31584613
## [795,] 1.55739207 0.65692307
## [796,] 2.29164676 0.55058918
## [797,] 0.93058450 0.66693521
## [798,] 0.73500205 0.58323908
## [799,] 0.67446368 0.94021808
## [800,] 1.08547387 1.37633270
## [801,] 0.68245483 0.69545837
## [802,] 0.50179757 0.62475886
## [803,] 0.61910098 0.73677167
## [804,] 1.49026500 0.80969062
## [805,] 1.45179297 0.89987098
## [806,] 0.54740790 0.58132030
## [807,] 0.86205726 0.85138191
## [808,] 0.59083511 0.52479279
## [809,] 0.85112129 1.36740351
## [810,] 0.70658559 0.97233147
## [811,] 0.81253622 1.15041181
## [812,] 0.41450782 1.41524205
## [813,] 0.82091427 0.37259115
## [814,] 0.52376194 1.41685258
## [815,] 0.77114039 1.60862863
## [816,] 1.22465591 0.59073340
## [817,] 0.95781654 0.95271945
## [818,] 0.63472191 1.11115609
## [819,] 0.76358623 1.06704974
## [820,] 0.59707072 0.71513485
## [821,] 0.85871503 2.37412511
## [822,] 0.64158975 0.80347374
## [823,] 1.55205644 0.35447264
## [824,] 0.90838222 0.61772986
## [825,] 3.51577286 1.58348915
## [826,] 0.33166547 2.33653491
## [827,] 0.63901842 1.02534814
## [828,] 0.31128520 5.41353508
## [829,] 0.51843339 0.97257635
## [830,] 0.55436876 0.61379923
## [831,] 0.58574315 1.21869541
## [832,] 0.29169072 1.28705874
## [833,] 1.86278887 0.71840808
## [834,] 0.42041615 0.79215627
## [835,] 1.02407234 0.73595143
## [836,] 2.00215929 0.92939937
## [837,] 0.44559268 0.71634208
## [838,] 0.89504085 1.65689160
## [839,] 1.02535354 1.09895574
## [840,] 0.60917067 1.82510686
## [841,] 0.69057386 0.26323949
## [842,] 0.43071677 1.19993542
## [843,] 1.30753005 0.95535958
## [844,] 0.13777656 1.44846464
## [845,] 0.92258916 0.55621499
## [846,] 0.44873757 1.04286950
## [847,] 0.64279155 0.79864140
## [848,] 2.08286281 0.59351539
## [849,] 0.71094494 1.01808404
## [850,] 0.48195024 0.93288414
## [851,] 1.80405854 0.87047101
## [852,] 2.52854446 1.11100840
## [853,] 1.21928920 0.70995115
## [854,] 3.96601672 0.50853007
## [855,] 2.14479592 1.87646838
## [856,] 1.90412609 0.18533800
## [857,] 1.85090905 3.00966631
## [858,] 0.67214844 4.65391294
## [859,] 0.70156410 1.01428882
## [860,] 0.29351715 0.37085724
## [861,] 1.32320359 0.47168272
## [862,] 1.27413952 0.55207094
## [863,] 0.77478015 1.98764433
## [864,] 3.09711533 1.80370131
## [865,] 1.01547136 0.74631698
## [866,] 0.82466584 0.21102808
## [867,] 0.61803730 2.36629347
## [868,] 3.46854118 1.11793801
## [869,] 1.09256390 0.78005281
## [870,] 0.54059526 0.50013181
## [871,] 0.32085582 0.60466249
## [872,] 1.05916034 1.70654660
## [873,] 1.13585932 1.12630061
## [874,] 2.21729981 0.58453025
## [875,] 0.68275123 0.29496890
## [876,] 2.34421459 0.75510598
## [877,] 0.96660792 0.76149712
## [878,] 0.37156579 0.69832164
## [879,] 0.92663880 0.47332541
## [880,] 0.31474644 0.62203251
## [881,] 0.66612502 1.38598333
## [882,] 0.79651824 0.67247826
## [883,] 1.87018804 0.13356866
## [884,] 0.45754262 0.88423297
## [885,] 0.65014849 0.47032362
## [886,] 0.77285185 0.86075659
## [887,] 0.56272327 1.06181624
## [888,] 0.44374699 2.60851317
## [889,] 2.04456279 0.59525391
## [890,] 1.13371743 0.67189767
## [891,] 1.46390299 1.77383855
## [892,] 0.13512929 1.72057250
## [893,] 1.68376913 1.00940365
## [894,] 0.34731932 1.01305001
## [895,] 0.65650483 0.52295558
## [896,] 0.63410093 0.75147133
## [897,] 0.43276531 0.55611830
## [898,] 1.17889584 1.29308886
## [899,] 0.67801612 1.09922046
## [900,] 1.48300657 0.97460796
## [901,] 1.25386211 0.38496759
## [902,] 0.61074999 1.96542454
## [903,] 0.82498370 0.76577511
## [904,] 0.30190158 0.58061735
## [905,] 0.54192490 0.32129647
## [906,] 0.96739136 0.65171137
## [907,] 1.20244357 0.54517471
## [908,] 1.04092612 0.90224335
## [909,] 0.48427322 1.70539349
## [910,] 0.37291688 0.60446502
## [911,] 0.54586214 0.75045415
## [912,] 0.90915348 0.30176811
## [913,] 0.93397569 0.45377279
## [914,] 1.23809691 0.67378531
## [915,] 0.48201505 0.70064244
## [916,] 0.84143006 0.80556158
## [917,] 0.83494952 0.48739706
## [918,] 0.79742960 0.33584395
## [919,] 3.93054649 0.83905524
## [920,] 0.87783594 0.43348257
## [921,] 0.74400258 0.63340826
## [922,] 0.32285320 0.81455291
## [923,] 0.41764934 0.72273306
## [924,] 1.75716863 1.56628687
## [925,] 0.54933241 1.35605563
## [926,] 0.40654202 0.79134487
## [927,] 0.63489621 1.53863858
## [928,] 0.51119648 0.29266433
## [929,] 1.68831206 0.47220610
## [930,] 0.49666141 0.61521745
## [931,] 0.65066219 2.64083693
## [932,] 1.04086558 1.02466130
## [933,] 1.32745423 1.53111315
## [934,] 1.64521405 1.40051272
## [935,] 0.96523879 2.70010459
## [936,] 0.76993862 0.98871002
## [937,] 0.52338568 0.54784253
## [938,] 1.09644134 1.51289873
## [939,] 2.53652953 0.81627510
## [940,] 0.75051629 0.35489278
## [941,] 0.78647710 0.44349760
## [942,] 1.45006728 1.60062425
## [943,] 0.65091180 0.42958424
## [944,] 0.53840189 0.58103261
## [945,] 1.39281934 2.05964152
## [946,] 2.65325498 0.52977175
## [947,] 0.82029603 1.00323597
## [948,] 1.44925904 0.18480353
## [949,] 0.52661522 0.74925140
## [950,] 0.71190988 1.90055415
## [951,] 0.58813074 0.64582426
## [952,] 0.45174605 0.22329388
## [953,] 0.87243712 2.15840187
## [954,] 1.69847305 1.41647186
## [955,] 0.57155878 0.57540625
## [956,] 0.46530578 1.05019750
## [957,] 0.90364653 0.91433561
## [958,] 1.49305829 1.07958841
## [959,] 1.16296979 0.55226303
## [960,] 2.66687101 0.25149711
## [961,] 0.48123571 0.37420663
## [962,] 0.73301988 0.52189599
## [963,] 0.67200389 0.46139033
## [964,] 1.05429176 0.53024480
## [965,] 0.49128965 2.35295215
## [966,] 1.35870391 0.48767385
## [967,] 0.74061542 1.94095858
## [968,] 1.33282109 1.09400639
## [969,] 1.74403759 2.33643764
## [970,] 0.25952708 0.61846903
## [971,] 0.53501305 0.46448707
## [972,] 0.59442498 1.13909007
## [973,] 0.47885396 0.16679348
## [974,] 1.34537240 1.19695725
## [975,] 0.44915420 0.82809682
## [976,] 0.64168193 0.52860041
## [977,] 1.33925002 3.03503864
## [978,] 0.32839663 1.76664399
## [979,] 0.98462510 0.85480343
## [980,] 1.50052079 0.52674144
## [981,] 0.75205232 0.71317244
## [982,] 0.39176798 1.71079144
## [983,] 0.44813997 0.18411487
## [984,] 0.47527169 2.64295171
## [985,] 1.66440225 1.57954578
## [986,] 0.58575582 1.06720555
## [987,] 1.39610481 0.42259281
## [988,] 2.61574154 1.33563405
## [989,] 0.98945853 0.96495544
## [990,] 0.87999551 1.14208855
## [991,] 1.02656861 1.18866014
## [992,] 1.53156084 1.50200472
## [993,] 0.61494588 0.60978612
## [994,] 0.86038921 0.64911447
## [995,] 0.87326882 1.01885810
## [996,] 0.79084062 0.76181365
## [997,] 1.04971104 0.36455902
## [998,] 1.00068603 0.55395156
## [999,] 0.91927612 0.56423221
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
## 24 1 -1 0
##
## $terms
## pca_data_final ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_final, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_final 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_final <- vegdist(pca_data_final, species = "bray")
res_final <- pcoa(dist_final)
p02 <- as.data.frame(res_final$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_final,.)
p02$microsite <- ifelse(p02$microsite == "Density", "Shrub", p02$microsite)
pcoa_final <- ggplot(p02, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
labs(color = "Microsite")## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.pcoa_final <- pcoa_final + labs(x = "Shrub Density Gradient", y = "Community Composition") + scale_color_manual(
values = c("Shrub" = "#009900", "Open" = "#0066cc"), # Change color codes here
labels = c("Shrub" = "Shrub", "Open" = "Open")) + coord_fixed(ratio = 1)## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.pcoa_final
model020 <- betadisper(dist_final, env_final$microsite)
model020##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$microsite)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5034 0.4680
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model020)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 0.6184
## Residuals 22 0.64591 0.0293594permutest(model020,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 99 0.67
## Residuals 22 0.64591 0.0293594
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.66
## Open 0.6184model020.HSD <- TukeyHSD(model020)
model020.HSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## Open-Density -0.03534393 -0.1804147 0.1097268 0.6183989boxplot(model020)
model030 <- betadisper(dist_final, env_final$shrub_density)
model030##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$shrub_density)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4680 0.3766 0.4847 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model030)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.58003 0.11601 2.233 0.09554 .
## Residuals 18 0.93511 0.05195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model030,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.58003 0.11601 2.233 99 0.1
## Residuals 18 0.93511 0.05195
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.59000 0.82000
## 10 0.55757 0.66000
## 11 0.86321 0.60937
## 12
## 13
## 14model030.HSD <- TukeyHSD(model030)
boxplot(model030)
### Shows a significant difference between community composition of tested sites (Carrizo -> Cuyama -> Tecopa)
model040 <- betadisper(dist_final, env_final$site)
model040##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$site)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2734 0.3349 0.4558
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553anova(model040)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 0.04091 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permutest(model040,pairwise = TRUE, permutations = 99)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 99 0.03 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.410000 0.01
## Cuyama 0.425689 0.12
## Tecopa 0.016550 0.067329model040.HSD <- TukeyHSD(model040)
supp_plot <- boxplot(model040, xlab = "Region")
### Temperature Data 2022
Temp_2022 <- read_csv("Temp Data 2022.csv")## Rows: 52443 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): researcher, site, site_code, microsite, time_block
## dbl (5): microsite_number, pendent_number, pendent_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Temp_2022_final <- Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.###Ground Temp Data
Ground_Temp_2022 <- read_csv("Gradient Density Datasheet 2022.csv")## Rows: 695 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): site, site_code, date, microsite
## dbl (22): rep, microsite_number, shrub_ID, shrub_number, total_shrub, micros...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ground_Temp_2022_final <- Ground_Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) ## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.### Combine this with new_data### Aridity Data
aridity <- read_csv("regional_sites_2022.csv")## Rows: 51 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): state, desert, region, experiment, site_acronym, sub_site, site_co...
## dbl (7): lat, long, elevation, MAT, MAP, aridity, area_block_m2
## num (1): area_m2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.aridity <- aridity %>%
dplyr::select(site_code, aridity)final_2022<- photo%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.final_2022 <- final_2022 %>%
filter(common_name != "Blank")%>% filter(common_name != "No CV Result")
density_simple <- final_2022 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.#write.csv(density_simple, file = "density_simple_fixed.csv") Output density simple because it does not include the sites with 0 observations
density_simple_fixed <- read.csv("density_simple_fixed.csv")
density_simple_fixed <- density_simple_fixed[,-1]### This is for 2022 evenness
vegan_data <- animals_density ### Created new df for pca data
vegan_data <- vegan_data %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data <- vegan_data %>%
group_by(site_code, microsite) %>%
summarize(across(5:29, ~diversity(., index = "shannon")))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.evenness_data <- na.omit(evenness_data)
evenness_data <- evenness_data %>%
mutate(Average_Evenness = rowMeans(across(5:26)))
evenness_data <- evenness_data %>%
dplyr::select(site_code, microsite, Average_Evenness)
### Join evenness_data with density_simple data
new_data <- inner_join(density_simple_fixed, evenness_data, by = c("site_code", "microsite"))
### Combine new data with logger temp data from 2022
new_data <- inner_join(new_data, Temp_2022_final,by = c("site_code", "microsite"))
### Need to add ground temperature from hand recordings then 2022 is ready!
new_data <- inner_join(new_data, Ground_Temp_2022_final, by = c("site_code", "microsite"))
### Combine all data with aridity data of the sites we have
new_data <- inner_join(new_data, aridity, by = c("site_code"))
### 2022 data is now cleaned and ready to go### Clean up 2023 data so it can be properly combined with 2022 data
final_2023<- photo_2023%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.density_simple_2023 <- final_2023 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.write.csv(density_simple_2023, file = "density_simple_fixed_2023.csv")
### This is for 2023 evenness
vegan_data_2023 <- animals_density_2023 ### Created new df for pca data
vegan_data_2023 <- vegan_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data_2023 <- vegan_data_2023 %>%
group_by(site_code, microsite) %>%
summarize(across(5:27, ~diversity(., index = "shannon")))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.evenness_data_2023 <- na.omit(evenness_data_2023)
evenness_data_2023 <- evenness_data_2023 %>%
mutate(Average_Evenness = rowMeans(across(5:24)))
evenness_data_2023 <- evenness_data_2023 %>%
dplyr::select(site_code, microsite, Average_Evenness)
new_data_2023 <- inner_join(density_simple_2023, evenness_data_2023, by = c("site_code", "microsite"))
new_data_2023$site_code <- gsub("Tecopa_Shrub", "Tecopa_shrub", new_data_2023$site_code)
new_data_2023$site_code <- gsub("Tecopa_Open", "Tecopa_open", new_data_2023$site_code)
### Follow same steps as above 2022 data clean up. Start with logger temp, then ground temp, then aridity.
### Combine new_data_2023 to Temp_2023
Temp_2023 <- read_csv("Temp Data 2023.csv") %>% filter(!(temp > 50)) %>% filter(!(temp < -47))## Rows: 24760 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): researcher, site, site_code, microsite
## dbl (5): microsite_number, pendant_number, pendant_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Temp_2023_final <- Temp_2023 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.new_data_2023 <- inner_join(new_data_2023, Temp_2023_final, by = c("site_code", "microsite"))
### Combine Ground Temp Data from 2023
Ground_Temp_2023 <- read_csv("Gradient Density Datasheet 2023.csv")## New names:
## Rows: 587 Columns: 29
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): site, site_code, date, microsite dbl (25): rep, microsite_number,
## shrub_ID, shrub_number, total_shrub, micros...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...28`
## • `` -> `...29`Ground_Temp_2023_final <- Ground_Temp_2023 %>%
group_by(site_code, microsite, microsite_number) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) ## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.new_data_2023 <- inner_join(new_data_2023, Ground_Temp_2023_final, by = c("site_code", "microsite", "microsite_number"))
### Aridity data is already set up from 2022 data
new_data_2023 <- inner_join(new_data_2023, aridity, by = c("site_code"))
### 2023 Data is now cleaned and ready to be combined with 2022 datafinal_data <- rbind(new_data, new_data_2023)All Data is combined now into one clean dataframe that can be used to generate figures and statistics (Use final_data)
Stats for Shrub Density
library(ggpubr)
shapiro.test(final_data$animals)##
## Shapiro-Wilk normality test
##
## data: final_data$animals
## W = 0.62948, p-value = 1.551e-09ggqqplot(final_data$animals)
### Stats to show that the aridity across tested regions significantly varies. This needs to be one of the FIRST things you put in your results.
anova_result <- aov(aridity ~ site, data = final_data)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## site 2 103.52 51.76 3639 <2e-16 ***
## Residuals 43 0.61 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(anova_result, pairwise ~ site)## $emmeans
## site emmean SE df lower.CL upper.CL
## Carrizo 3.252 0.0319 43 3.188 3.316
## Cuyama 3.701 0.0298 43 3.641 3.762
## Tecopa 0.365 0.0298 43 0.305 0.425
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo - Cuyama -0.449 0.0436 43 -10.297 <.0001
## Carrizo - Tecopa 2.887 0.0436 43 66.147 <.0001
## Cuyama - Tecopa 3.336 0.0422 43 79.126 <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatestukey_result <- TukeyHSD(anova_result)
# View the Tukey's HSD results
print(tukey_result)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = aridity ~ site, data = final_data)
##
## $site
## diff lwr upr p adj
## Cuyama-Carrizo 0.4494073 0.3434585 0.5553561 0
## Tecopa-Carrizo -2.8870527 -2.9930014 -2.7811039 0
## Tecopa-Cuyama -3.3364600 -3.4388162 -3.2341037 0### Tukey test shows Cuyama less arid than Carrizo, Carrizo less arid than Tecopa, Cuyama less arid than Tecopa### Abundance
model1 <- glm(animals~shrub_density*site*year+aridity, family = "gaussian", data = final_data)
model1##
## Call: glm(formula = animals ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 7.551e+04 1.061e+04
## siteCuyama siteTecopa
## 4.027e+05 -9.065e+04
## year aridity
## -3.736e+01 3.297e+01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -1.559e+03 -1.211e+04
## shrub_density:year siteCuyama:year
## -5.245e+00 -1.991e+02
## siteTecopa:year shrub_density:siteCuyama:year
## 4.485e+01 7.703e-01
## shrub_density:siteTecopa:year
## 5.986e+00
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 765800
## Residual Deviance: 302900 AIC: 563anova(model1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## shrub_density 1 18242 44 747531 0.1586319
## site 2 139832 42 607700 0.0004923 ***
## year 1 137687 41 470013 0.0001076 ***
## aridity 1 55 40 469958 0.9380678
## shrub_density:site 2 3544 38 466414 0.8244495
## shrub_density:year 1 5580 37 460833 0.4355873
## site:year 2 155562 35 305272 0.0002090 ***
## shrub_density:site:year 2 2342 33 302929 0.8802245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e2 <- emmeans(model1, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse2## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 83.8 122 33 -164 332
## Cuyama 2022 235.1 176 33 -123 593
## Tecopa 2022 74.6 274 33 -482 631
## Carrizo 2023 15.7 111 33 -210 242
## Cuyama 2023 -27.6 176 33 -386 330
## Tecopa 2023 86.4 274 33 -470 643
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -151.28 73.5 33 -2.057 0.3336
## Carrizo year2022 - Tecopa year2022 9.25 391.4 33 0.024 1.0000
## Carrizo year2022 - Carrizo year2023 68.15 54.7 33 1.246 0.8109
## Carrizo year2022 - Cuyama year2023 111.45 73.5 33 1.516 0.6570
## Carrizo year2022 - Tecopa year2023 -2.59 391.4 33 -0.007 1.0000
## Cuyama year2022 - Tecopa year2022 160.53 446.7 33 0.359 0.9991
## Cuyama year2022 - Carrizo year2023 219.43 87.3 33 2.515 0.1490
## Cuyama year2022 - Cuyama year2023 262.73 47.9 33 5.480 0.0001
## Cuyama year2022 - Tecopa year2023 148.70 446.7 33 0.333 0.9994
## Tecopa year2022 - Carrizo year2023 58.90 378.5 33 0.156 1.0000
## Tecopa year2022 - Cuyama year2023 102.20 446.7 33 0.229 0.9999
## Tecopa year2022 - Tecopa year2023 -11.83 48.2 33 -0.245 0.9999
## Carrizo year2023 - Cuyama year2023 43.30 87.3 33 0.496 0.9960
## Carrizo year2023 - Tecopa year2023 -70.73 378.5 33 -0.187 1.0000
## Cuyama year2023 - Tecopa year2023 -114.04 446.7 33 -0.255 0.9998
##
## P value adjustment: tukey method for comparing a family of 6 estimatesshapiro.test(final_data$richness)##
## Shapiro-Wilk normality test
##
## data: final_data$richness
## W = 0.96052, p-value = 0.1201ggqqplot(final_data$richness)
### Richness Stats
model2 <- glm(richness~shrub_density*site*year +aridity, family = "gaussian", data = final_data)
model2##
## Call: glm(formula = richness ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## -1.049e+03 -9.984e+01
## siteCuyama siteTecopa
## 1.799e+04 -6.515e+03
## year aridity
## 5.227e-01 -5.391e-01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -2.708e+02 -1.922e+02
## shrub_density:year siteCuyama:year
## 4.939e-02 -8.895e+00
## siteTecopa:year shrub_density:siteCuyama:year
## 3.219e+00 1.339e-01
## shrub_density:siteTecopa:year
## 9.508e-02
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 575.9
## Residual Deviance: 79.32 AIC: 183.6anova_results2 <- aov(model2, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## shrub_density 1 27.132 44 548.78 0.0007803 ***
## site 2 164.760 42 384.02 1.305e-15 ***
## year 1 6.218 41 377.80 0.1077656
## aridity 1 1.307 40 376.50 0.4609209
## shrub_density:site 2 1.941 38 374.56 0.6678024
## shrub_density:year 1 3.261 37 371.29 0.2441039
## site:year 2 290.938 35 80.36 < 2.2e-16 ***
## shrub_density:site:year 2 1.034 33 79.32 0.8064261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results2)## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 27.13 27.13 11.287 0.00198 **
## site 2 164.76 82.38 34.272 8.82e-09 ***
## year 1 6.22 6.22 2.587 0.11729
## aridity 1 1.31 1.31 0.544 0.46613
## shrub_density:site 2 1.94 0.97 0.404 0.67106
## shrub_density:year 1 3.26 3.26 1.357 0.25246
## site:year 2 290.94 145.47 60.519 9.11e-12 ***
## shrub_density:site:year 2 1.03 0.52 0.215 0.80755
## Residuals 33 79.32 2.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e3 <- emmeans(model2, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse3 ### Ok now this all works.## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 6.356 1.97 33 2.346 10.37
## Cuyama 2022 12.443 2.85 33 6.649 18.24
## Tecopa 2022 0.208 4.43 33 -8.799 9.21
## Carrizo 2023 7.168 1.80 33 3.513 10.82
## Cuyama 2023 5.146 2.85 33 -0.648 10.94
## Tecopa 2023 4.797 4.43 33 -4.209 13.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -6.087 1.190 33 -5.116 0.0002
## Carrizo year2022 - Tecopa year2022 6.148 6.333 33 0.971 0.9239
## Carrizo year2022 - Carrizo year2023 -0.813 0.885 33 -0.918 0.9390
## Carrizo year2022 - Cuyama year2023 1.210 1.190 33 1.017 0.9090
## Carrizo year2022 - Tecopa year2023 1.558 6.333 33 0.246 0.9999
## Cuyama year2022 - Tecopa year2022 12.235 7.229 33 1.692 0.5461
## Cuyama year2022 - Carrizo year2023 5.274 1.412 33 3.736 0.0085
## Cuyama year2022 - Cuyama year2023 7.297 0.776 33 9.405 <.0001
## Cuyama year2022 - Tecopa year2023 7.645 7.229 33 1.058 0.8944
## Tecopa year2022 - Carrizo year2023 -6.960 6.125 33 -1.136 0.8626
## Tecopa year2022 - Cuyama year2023 -4.938 7.229 33 -0.683 0.9826
## Tecopa year2022 - Tecopa year2023 -4.590 0.781 33 -5.880 <.0001
## Carrizo year2023 - Cuyama year2023 2.022 1.412 33 1.432 0.7076
## Carrizo year2023 - Tecopa year2023 2.371 6.125 33 0.387 0.9988
## Cuyama year2023 - Tecopa year2023 0.348 7.229 33 0.048 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimatesggqqplot(final_data$Average_Evenness)
### Evenness
model3 <- glm(Average_Evenness~shrub_density*site*year + aridity, family = "gaussian", data = final_data)
model3##
## Call: glm(formula = Average_Evenness ~ shrub_density * site * year +
## aridity, family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 1.175e+02 -8.606e+00
## siteCuyama siteTecopa
## 2.013e+02 -3.866e+02
## year aridity
## -5.801e-02 -2.568e-02
## shrub_density:siteCuyama shrub_density:siteTecopa
## 1.008e+01 -9.806e+00
## shrub_density:year siteCuyama:year
## 4.257e-03 -9.952e-02
## siteTecopa:year shrub_density:siteCuyama:year
## 1.911e-01 -4.981e-03
## shrub_density:siteTecopa:year
## 4.848e-03
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.02358 AIC: -190anova_results3 <- aov(model3, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## shrub_density 1 0.020430 44 0.285923 8.936e-08 ***
## site 2 0.011457 42 0.274466 0.0003297 ***
## year 1 0.000058 41 0.274408 0.7763951
## aridity 1 0.000022 40 0.274386 0.8618492
## shrub_density:site 2 0.001475 38 0.272911 0.3562435
## shrub_density:year 1 0.000932 37 0.271980 0.2535206
## site:year 2 0.242134 35 0.029845 < 2.2e-16 ***
## shrub_density:site:year 2 0.006265 33 0.023580 0.0124756 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results3)## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 0.02043 0.02043 28.592 6.62e-06 ***
## site 2 0.01146 0.00573 8.017 0.00145 **
## year 1 0.00006 0.00006 0.081 0.77817
## aridity 1 0.00002 0.00002 0.030 0.86291
## shrub_density:site 2 0.00148 0.00074 1.032 0.36746
## shrub_density:year 1 0.00093 0.00093 1.304 0.26174
## site:year 2 0.24213 0.12107 169.432 < 2e-16 ***
## shrub_density:site:year 2 0.00627 0.00313 4.384 0.02049 *
## Residuals 33 0.02358 0.00071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e4 <- emmeans(model3, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse4 ### Need to double check emmeans it does not match the figure## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.1186 0.0340 33 0.04944 0.188
## Cuyama 2022 0.2302 0.0491 33 0.13028 0.330
## Tecopa 2022 -0.0251 0.0763 33 -0.18038 0.130
## Carrizo 2023 0.0856 0.0310 33 0.02254 0.149
## Cuyama 2023 0.0684 0.0491 33 -0.03150 0.168
## Tecopa 2023 0.1615 0.0763 33 0.00617 0.317
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -0.1116 0.0205 33 -5.440 0.0001
## Carrizo year2022 - Tecopa year2022 0.1437 0.1092 33 1.316 0.7744
## Carrizo year2022 - Carrizo year2023 0.0330 0.0153 33 2.165 0.2809
## Carrizo year2022 - Cuyama year2023 0.0502 0.0205 33 2.446 0.1701
## Carrizo year2022 - Tecopa year2023 -0.0429 0.1092 33 -0.393 0.9987
## Cuyama year2022 - Tecopa year2022 0.2553 0.1246 33 2.048 0.3384
## Cuyama year2022 - Carrizo year2023 0.1446 0.0243 33 5.941 <.0001
## Cuyama year2022 - Cuyama year2023 0.1618 0.0134 33 12.094 <.0001
## Cuyama year2022 - Tecopa year2023 0.0687 0.1246 33 0.551 0.9934
## Tecopa year2022 - Carrizo year2023 -0.1106 0.1056 33 -1.048 0.8980
## Tecopa year2022 - Cuyama year2023 -0.0935 0.1246 33 -0.750 0.9738
## Tecopa year2022 - Tecopa year2023 -0.1865 0.0135 33 -13.861 <.0001
## Carrizo year2023 - Cuyama year2023 0.0172 0.0243 33 0.705 0.9800
## Carrizo year2023 - Tecopa year2023 -0.0759 0.1056 33 -0.719 0.9782
## Cuyama year2023 - Tecopa year2023 -0.0931 0.1246 33 -0.747 0.9743
##
## P value adjustment: tukey method for comparing a family of 6 estimatesData Viz
### All below in chunk are by site
### Abundance vs Density
abundance <- ggplot(final_data, aes(shrub_density, animals, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance ## `geom_smooth()` using formula = 'y ~ x'
richness <- ggplot(final_data, aes(shrub_density, richness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness", color = "Region")
richness## `geom_smooth()` using formula = 'y ~ x'
Evenness <- ggplot(final_data, aes(shrub_density, Average_Evenness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
Evenness## `geom_smooth()` using formula = 'y ~ x'
library(patchwork)##
## Attaching package: 'patchwork'
##
## The following object is masked from 'package:MASS':
##
## areadensity_plot <- abundance/richness/Evenness
density_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Stats for Average Temperature
### Abundance and temperature
model4 <- glm(animals~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model4##
## Call: glm(formula = animals ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -6.423e+08
## mean_temp
## 2.677e+07
## siteCuyama
## 4.148e+08
## siteTecopa
## -4.661e+08
## year
## 3.175e+05
## mean_humidity
## 2.602e+07
## mean_temp:siteCuyama
## -1.626e+07
## mean_temp:siteTecopa
## 1.146e+07
## mean_temp:year
## -1.323e+04
## siteCuyama:year
## -2.051e+05
## siteTecopa:year
## 2.304e+05
## mean_temp:mean_humidity
## -1.084e+06
## siteCuyama:mean_humidity
## -1.764e+07
## siteTecopa:mean_humidity
## -7.572e+02
## year:mean_humidity
## -1.286e+04
## mean_temp:siteCuyama:year
## 8.037e+03
## mean_temp:siteTecopa:year
## -5.664e+03
## mean_temp:siteCuyama:mean_humidity
## 6.983e+05
## mean_temp:siteTecopa:mean_humidity
## 2.925e+01
## mean_temp:year:mean_humidity
## 5.360e+02
## siteCuyama:year:mean_humidity
## 8.722e+03
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -3.452e+02
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 765800
## Residual Deviance: 57540 AIC: 504.6anova_results4 <- aov(model4, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model4, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## mean_temp 1 69722 44 696052 6.934e-08
## site 2 82375 42 613676 3.456e-08
## year 1 184771 41 428905 < 2.2e-16
## mean_humidity 1 1138 40 427767 0.4907466
## mean_temp:site 2 108585 38 319182 1.461e-10
## mean_temp:year 1 7837 37 311345 0.0706052
## site:year 2 33153 35 278192 0.0009931
## mean_temp:mean_humidity 1 962 34 277230 0.5264108
## site:mean_humidity 2 5743 32 271487 0.3018453
## year:mean_humidity 1 7165 31 264322 0.0838464
## mean_temp:site:year 2 6207 29 258114 0.2739932
## mean_temp:site:mean_humidity 2 160588 27 97526 2.845e-15
## mean_temp:year:mean_humidity 1 367 26 97159 0.6956019
## site:year:mean_humidity 1 36297 25 60862 9.978e-05
## mean_temp:site:year:mean_humidity 1 3326 24 57536 0.2388432
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity
## mean_temp:site ***
## mean_temp:year .
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity
## year:mean_humidity .
## mean_temp:site:year
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results4)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 69722 69722 28.639 1.50e-05 ***
## site 2 82375 41188 16.919 2.26e-05 ***
## year 1 184771 184771 75.898 4.80e-09 ***
## mean_humidity 1 1138 1138 0.468 0.500364
## mean_temp:site 2 108585 54292 22.301 2.76e-06 ***
## mean_temp:year 1 7837 7837 3.219 0.084892 .
## site:year 2 33153 16577 6.809 0.004358 **
## mean_temp:mean_humidity 1 962 962 0.395 0.535285
## site:mean_humidity 2 5743 2872 1.180 0.323946
## year:mean_humidity 1 7165 7165 2.943 0.098614 .
## mean_temp:site:year 2 6207 3104 1.275 0.297010
## mean_temp:site:mean_humidity 2 160588 80294 32.982 9.74e-08 ***
## mean_temp:year:mean_humidity 1 367 367 0.151 0.701105
## site:year:mean_humidity 1 36297 36297 14.910 0.000707 ***
## Residuals 25 60862 2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e5 <- emmeans(model4, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse5## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 -938.5 619 24 -2217 340
## Cuyama 2022 1714.0 146 24 1414 2015
## Tecopa 2022 -89833.5 55883 24 -205170 25503
## Carrizo 2023 37.5 261 24 -501 576
## Cuyama 2023 1006.2 1997 24 -3116 5128
## Tecopa 2023 -81.9 3323 24 -6940 6776
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -2652 636 24 -4.170 0.0041
## Carrizo year2022 - Tecopa year2022 88895 55335 24 1.606 0.6024
## Carrizo year2022 - Carrizo year2023 -976 672 24 -1.453 0.6959
## Carrizo year2022 - Cuyama year2023 -1945 2091 24 -0.930 0.9347
## Carrizo year2022 - Tecopa year2023 -857 3380 24 -0.253 0.9998
## Cuyama year2022 - Tecopa year2022 91548 55883 24 1.638 0.5828
## Cuyama year2022 - Carrizo year2023 1676 299 24 5.615 0.0001
## Cuyama year2022 - Cuyama year2023 708 2003 24 0.353 0.9992
## Cuyama year2022 - Tecopa year2023 1796 3326 24 0.540 0.9938
## Tecopa year2022 - Carrizo year2023 -89871 55876 24 -1.608 0.6012
## Tecopa year2022 - Cuyama year2023 -90840 55919 24 -1.624 0.5913
## Tecopa year2022 - Tecopa year2023 -89752 57406 24 -1.563 0.6288
## Carrizo year2023 - Cuyama year2023 -969 2014 24 -0.481 0.9964
## Carrizo year2023 - Tecopa year2023 119 3333 24 0.036 1.0000
## Cuyama year2023 - Tecopa year2023 1088 3877 24 0.281 0.9997
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Richness and temperature
model5 <- glm(richness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model5##
## Call: glm(formula = richness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## 4.250e+05
## mean_temp
## -2.300e+04
## siteCuyama
## 8.940e+06
## siteTecopa
## 1.097e+05
## year
## -2.101e+02
## mean_humidity
## -1.721e+04
## mean_temp:siteCuyama
## -3.999e+05
## mean_temp:siteTecopa
## 5.848e+03
## mean_temp:year
## 1.137e+01
## siteCuyama:year
## -4.421e+03
## siteTecopa:year
## -5.411e+01
## mean_temp:mean_humidity
## 9.181e+02
## siteCuyama:mean_humidity
## -2.453e+05
## siteTecopa:mean_humidity
## 4.210e+00
## year:mean_humidity
## 8.507e+00
## mean_temp:siteCuyama:year
## 1.977e+02
## mean_temp:siteTecopa:year
## -2.896e+00
## mean_temp:siteCuyama:mean_humidity
## 1.097e+04
## mean_temp:siteTecopa:mean_humidity
## -3.693e-02
## mean_temp:year:mean_humidity
## -4.539e-01
## siteCuyama:year:mean_humidity
## 1.213e+02
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -5.424e+00
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 575.9
## Residual Deviance: 21.07 AIC: 140.6anova_results5 <- aov(model5, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model5, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## mean_temp 1 212.116 44 363.80 < 2.2e-16
## site 2 13.679 42 350.12 0.0004135
## year 1 101.722 41 248.40 < 2.2e-16
## mean_humidity 1 36.989 40 211.41 8.530e-11
## mean_temp:site 2 105.199 38 106.21 < 2.2e-16
## mean_temp:year 1 22.336 37 83.87 4.559e-07
## site:year 2 17.597 35 66.28 4.442e-05
## mean_temp:mean_humidity 1 1.362 34 64.91 0.2129439
## site:mean_humidity 2 6.395 32 58.52 0.0261978
## year:mean_humidity 1 0.241 31 58.28 0.6000577
## mean_temp:site:year 2 7.846 29 50.43 0.0114660
## mean_temp:site:mean_humidity 2 3.530 27 46.90 0.1339519
## mean_temp:year:mean_humidity 1 16.254 26 30.65 1.686e-05
## site:year:mean_humidity 1 8.757 25 21.89 0.0015875
## mean_temp:site:year:mean_humidity 1 0.821 24 21.07 0.3334469
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity *
## year:mean_humidity
## mean_temp:site:year *
## mean_temp:site:mean_humidity
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity **
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results5)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 212.12 212.12 242.238 2.27e-14 ***
## site 2 13.68 6.84 7.811 0.002316 **
## year 1 101.72 101.72 116.168 6.92e-11 ***
## mean_humidity 1 36.99 36.99 42.241 8.30e-07 ***
## mean_temp:site 2 105.20 52.60 60.069 2.83e-10 ***
## mean_temp:year 1 22.34 22.34 25.508 3.27e-05 ***
## site:year 2 17.60 8.80 10.048 0.000627 ***
## mean_temp:mean_humidity 1 1.36 1.36 1.555 0.223910
## site:mean_humidity 2 6.39 3.20 3.652 0.040617 *
## year:mean_humidity 1 0.24 0.24 0.276 0.604211
## mean_temp:site:year 2 7.85 3.92 4.480 0.021736 *
## mean_temp:site:mean_humidity 2 3.53 1.76 2.015 0.154342
## mean_temp:year:mean_humidity 1 16.25 16.25 18.563 0.000224 ***
## site:year:mean_humidity 1 8.76 8.76 10.000 0.004075 **
## Residuals 25 21.89 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e6 <- emmeans(model5, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse6## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 12.10 11.85 24 -12.360 36.56
## Cuyama 2022 1.88 2.80 24 -3.898 7.67
## Tecopa 2022 161.29 1069.41 24 -2045.856 2368.43
## Carrizo 2023 10.67 4.99 24 0.371 20.97
## Cuyama 2023 142.87 38.22 24 63.983 221.75
## Tecopa 2023 33.32 63.59 24 -97.914 164.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 10.22 12.17 24 0.839 0.9570
## Carrizo year2022 - Tecopa year2022 -149.19 1058.93 24 -0.141 1.0000
## Carrizo year2022 - Carrizo year2023 1.43 12.86 24 0.111 1.0000
## Carrizo year2022 - Cuyama year2023 -130.77 40.02 24 -3.268 0.0340
## Carrizo year2022 - Tecopa year2023 -21.22 64.68 24 -0.328 0.9994
## Cuyama year2022 - Tecopa year2022 -159.41 1069.41 24 -0.149 1.0000
## Cuyama year2022 - Carrizo year2023 -8.79 5.71 24 -1.538 0.6444
## Cuyama year2022 - Cuyama year2023 -140.98 38.32 24 -3.679 0.0133
## Cuyama year2022 - Tecopa year2023 -31.44 63.65 24 -0.494 0.9959
## Tecopa year2022 - Carrizo year2023 150.62 1069.27 24 0.141 1.0000
## Tecopa year2022 - Cuyama year2023 18.42 1070.09 24 0.017 1.0000
## Tecopa year2022 - Tecopa year2023 127.97 1098.55 24 0.116 1.0000
## Carrizo year2023 - Cuyama year2023 -132.19 38.54 24 -3.430 0.0236
## Carrizo year2023 - Tecopa year2023 -22.65 63.78 24 -0.355 0.9992
## Cuyama year2023 - Tecopa year2023 109.55 74.19 24 1.477 0.6816
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Evenness and temperature
model6 <- glm(Average_Evenness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model6##
## Call: glm(formula = Average_Evenness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -2.924e+04
## mean_temp
## 1.075e+03
## siteCuyama
## 1.978e+05
## siteTecopa
## 1.918e+04
## year
## 1.445e+01
## mean_humidity
## 1.089e+03
## mean_temp:siteCuyama
## -8.739e+03
## mean_temp:siteTecopa
## -7.814e+02
## mean_temp:year
## -5.312e-01
## siteCuyama:year
## -9.782e+01
## siteTecopa:year
## -9.486e+00
## mean_temp:mean_humidity
## -3.940e+01
## siteCuyama:mean_humidity
## -6.112e+03
## siteTecopa:mean_humidity
## -1.112e-02
## year:mean_humidity
## -5.383e-01
## mean_temp:siteCuyama:year
## 4.321e+00
## mean_temp:siteTecopa:year
## 3.864e-01
## mean_temp:siteCuyama:mean_humidity
## 2.686e+02
## mean_temp:siteTecopa:mean_humidity
## 5.490e-04
## mean_temp:year:mean_humidity
## 1.948e-02
## siteCuyama:year:mean_humidity
## 3.022e+00
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -1.328e-01
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.001288 AIC: -305.7anova_results6 <- aov(model6, test = "Chisq")## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregardedanova(model6, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## mean_temp 1 0.011046 44 0.295307 < 2.2e-16
## site 2 0.073313 42 0.221994 < 2.2e-16
## year 1 0.068416 41 0.153577 < 2.2e-16
## mean_humidity 1 0.004958 40 0.148620 < 2.2e-16
## mean_temp:site 2 0.112108 38 0.036512 < 2.2e-16
## mean_temp:year 1 0.002065 37 0.034447 5.578e-10
## site:year 2 0.012030 35 0.022417 < 2.2e-16
## mean_temp:mean_humidity 1 0.000145 34 0.022272 0.1001135
## site:mean_humidity 2 0.000774 32 0.021498 0.0007366
## year:mean_humidity 1 0.001450 31 0.020048 2.032e-07
## mean_temp:site:year 2 0.003008 29 0.017040 6.819e-13
## mean_temp:site:mean_humidity 2 0.005407 27 0.011633 < 2.2e-16
## mean_temp:year:mean_humidity 1 0.007064 26 0.004569 < 2.2e-16
## site:year:mean_humidity 1 0.002788 25 0.001781 5.712e-13
## mean_temp:site:year:mean_humidity 1 0.000492 24 0.001288 0.0024604
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity ***
## year:mean_humidity ***
## mean_temp:site:year ***
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anova_results6)## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 0.01105 0.01105 155.089 3.23e-12 ***
## site 2 0.07331 0.03666 514.655 < 2e-16 ***
## year 1 0.06842 0.06842 960.555 < 2e-16 ***
## mean_humidity 1 0.00496 0.00496 69.605 1.08e-08 ***
## mean_temp:site 2 0.11211 0.05605 786.988 < 2e-16 ***
## mean_temp:year 1 0.00206 0.00206 28.990 1.38e-05 ***
## site:year 2 0.01203 0.00602 84.452 7.58e-12 ***
## mean_temp:mean_humidity 1 0.00015 0.00015 2.038 0.165804
## site:mean_humidity 2 0.00077 0.00039 5.437 0.010954 *
## year:mean_humidity 1 0.00145 0.00145 20.352 0.000132 ***
## mean_temp:site:year 2 0.00301 0.00150 21.114 4.26e-06 ***
## mean_temp:site:mean_humidity 2 0.00541 0.00270 37.958 2.66e-08 ***
## mean_temp:year:mean_humidity 1 0.00706 0.00706 99.178 3.48e-10 ***
## site:year:mean_humidity 1 0.00279 0.00279 39.150 1.52e-06 ***
## Residuals 25 0.00178 0.00007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e7 <- emmeans(model6, pairwise~site*year)## NOTE: Results may be misleading due to involvement in interactionse7## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.2333 0.0927 24 0.0420 0.4246
## Cuyama 2022 -0.0603 0.0218 24 -0.1054 -0.0153
## Tecopa 2022 -0.0817 8.3624 24 -17.3409 17.1775
## Carrizo 2023 0.0399 0.0390 24 -0.0407 0.1204
## Cuyama 2023 2.0244 0.2989 24 1.4075 2.6413
## Tecopa 2023 -0.0993 0.4972 24 -1.1255 0.9269
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 0.2937 0.0952 24 3.084 0.0508
## Carrizo year2022 - Tecopa year2022 0.3150 8.2805 24 0.038 1.0000
## Carrizo year2022 - Carrizo year2023 0.1935 0.1005 24 1.924 0.4125
## Carrizo year2022 - Cuyama year2023 -1.7911 0.3129 24 -5.724 0.0001
## Carrizo year2022 - Tecopa year2023 0.3326 0.5058 24 0.658 0.9849
## Cuyama year2022 - Tecopa year2022 0.0213 8.3625 24 0.003 1.0000
## Cuyama year2022 - Carrizo year2023 -0.1002 0.0447 24 -2.241 0.2565
## Cuyama year2022 - Cuyama year2023 -2.0848 0.2997 24 -6.957 <.0001
## Cuyama year2022 - Tecopa year2023 0.0390 0.4977 24 0.078 1.0000
## Tecopa year2022 - Carrizo year2023 -0.1215 8.3614 24 -0.015 1.0000
## Tecopa year2022 - Cuyama year2023 -2.1061 8.3678 24 -0.252 0.9998
## Tecopa year2022 - Tecopa year2023 0.0176 8.5903 24 0.002 1.0000
## Carrizo year2023 - Cuyama year2023 -1.9846 0.3014 24 -6.584 <.0001
## Carrizo year2023 - Tecopa year2023 0.1392 0.4987 24 0.279 0.9997
## Cuyama year2023 - Tecopa year2023 2.1237 0.5801 24 3.661 0.0139
##
## P value adjustment: tukey method for comparing a family of 6 estimates### Ambient Temperature Plots
### Plots of temp might look super ugly.
abundance_temp <- ggplot(final_data, aes(mean_temp, animals)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") +
labs(x = "Average Temperature (C)", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")
abundance_temp## `geom_smooth()` using formula = 'y ~ x'
richness_temp <- ggplot(final_data, aes(mean_temp, richness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme(axis.title.x = element_blank()) + labs(tag = "B")+ theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Average Temperature (C)", y = "Richness") + theme(axis.title.x = element_blank())
richness_temp## `geom_smooth()` using formula = 'y ~ x'
evenness_temp <- ggplot(final_data, aes(mean_temp, Average_Evenness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Mean Temperature (°C)"), y = "Mean Evenness")
evenness_temp## `geom_smooth()` using formula = 'y ~ x'
temp_plot <- abundance_temp/richness_temp/evenness_temp
temp_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Other posisble figure options
abundance_2 <- ggplot(final_data, aes(shrub_density, animals)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance_2## `geom_smooth()` using formula = 'y ~ x'
richness_2 <- ggplot(final_data, aes(shrub_density, richness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness")
richness_2## `geom_smooth()` using formula = 'y ~ x'
evenness_2 <- ggplot(final_data, aes(shrub_density, Average_Evenness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
plot1.1 <- abundance_2/richness_2/evenness_2
plot1.1## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Percent proportion Figure
#write.csv(density_obvs_final, file = "Animal Observations.csv")
scientific_names <- read.csv("Animal Observations.csv")
plot3 <- ggplot(scientific_names, aes(scientific_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot3 <-plot3 + scale_fill_manual(values = c("#009900", "#0066cc"))# Assuming you have a dataset named scientific_names with columns: scientific_name, percent_presence, microsite
# Create a new variable to specify the ordering based on presence in density or open areas
scientific_names$microsite <- ifelse(scientific_names$microsite == "Density", "Shrub", scientific_names$microsite)
scientific_names <- scientific_names %>%
mutate(ordering_var = ifelse(microsite == "Shrub", 1, 2)) %>%
arrange(ordering_var, desc(percent_presence))
# Create the histogram-style figure with reordering
plot3.2 <- ggplot(scientific_names, aes(fct_inorder(scientific_name), percent_presence, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Percent Proportion") +
labs(fill = "Microsite") +
scale_fill_manual(values = c("#0066cc", "#009900")) + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))
# Reorder the x-axis labels
plot3.2 <- plot3.2 + scale_x_discrete(limits = rev(levels(scientific_names$scientific_name)))
# Plot the figure
print(plot3.2)
Final Figures for Publication
### Figure 1: Abundance, Richness, Evenness vs Shrub Density
plot1.1## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 2: Abundance, Richness, Evenness vs Average Temperature
temp_plot## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 3: PCOA of Communities
pcoa_final
### Figure 4: Percent Proportion of Vertebrate species
plot3.2
#write.csv(final_data, file = "Final Data.csv")Data Together
### Test figure with formula (Shrub Density)
final_data <- final_data %>%
mutate(year = as.character(year))
### Density v abundance
ggplot(final_data, aes(shrub_density, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Density v Richness
ggplot(final_data, aes(shrub_density, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Density v Evenness
ggplot(final_data, aes(shrub_density, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Aridity v Abundance
ggplot(final_data, aes(aridity, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Aridity v Richness
ggplot(final_data, aes(aridity, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Aridity v Evenness
ggplot(final_data, aes(aridity, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Stats
#install.packages("lme4", type = "source")
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(performance)
#simple
m1 <- lmer(animals ~ shrub_density + (1|year) + aridity, data = final_data)
check_collinearity(m1)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]anova(m1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 14370 14370 1 42.018 1.2164 0.27635
## aridity 114226 114226 1 42.010 9.6692 0.00336 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lsmeansLT(m1)## Least Squares Means table:
##
## Estimate Std. Error df t value lower upper Pr(>|t|)
##
## Confidence level: 95%
## Degrees of freedom method: Satterthwaiteranova(m1) # I think this is to test for random effects across year?## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## animals ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -271.70 553.40
## (1 | year) 4 -274.97 557.94 6.5368 1 0.01057 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m2 <- lmer(richness ~ shrub_density + (1|year) + aridity, data = final_data)## boundary (singular) fit: see help('isSingular')check_collinearity(m2)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]anova(m2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 17.096 17.096 1 43 1.8397 0.1820664
## aridity 149.184 149.184 1 43 16.0534 0.0002401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ranova(m2)## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## richness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -116.83 243.66
## (1 | year) 4 -116.83 241.66 -8.5265e-14 1 1m3 <- lmer(Average_Evenness ~ shrub_density + (1|year) + aridity, data = final_data)## boundary (singular) fit: see help('isSingular')check_collinearity(m3)## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]anova(m3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 0.0215532 0.0215532 1 43 3.2712 0.0775 .
## aridity 0.0026079 0.0026079 1 43 0.3958 0.5326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ranova(m3)## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## Average_Evenness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 39.082 -68.164
## (1 | year) 4 39.082 -70.164 -1.4211e-14 1 1Test Trophic Level?
trophic <- read.csv("Animal Observations.csv")
ggplot(trophic, aes(microsite, captures, color = Trophic)) +
geom_boxplot() +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
m9<- glm(total ~ microsite * Trophic, family = "poisson", data = trophic)
anova(m9, test = "Chisq")## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## Trophic 2 7884.0 52 19907 < 2.2e-16 ***
## microsite:Trophic 2 2.0 50 19905 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e9 <- emmeans(m9, pairwise~microsite|Trophic)
e9## $emmeans
## Trophic = Carnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0767 Inf 2.91 3.21
## Open 3.18 0.0769 Inf 3.03 3.33
##
## Trophic = Herbivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 5.62 0.0173 Inf 5.59 5.66
## Open 5.71 0.0174 Inf 5.68 5.74
##
## Trophic = Omnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0685 Inf 2.92 3.19
## Open 3.00 0.0788 Inf 2.85 3.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## Trophic = Carnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.1276 0.1086 Inf -1.175 0.2400
##
## Trophic = Herbivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.0849 0.0245 Inf -3.461 0.0005
##
## Trophic = Omnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open 0.0567 0.1044 Inf 0.543 0.5869
##
## Results are given on the log (not the response) scale.Seperate everything by year (Nonlinear better for 2022)
final_data_2022 <- final_data %>%
filter(year == "2022")
shapiro.test(final_data_2022$animals)##
## Shapiro-Wilk normality test
##
## data: final_data_2022$animals
## W = 0.76017, p-value = 7.037e-05ggqqplot(final_data_2022$animals)
n1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n1##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -27.7891 0.6522 54.0811
## shrub_density:aridity
## 1.6648
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 376700 AIC: 310anova(n1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2314117
## aridity 1 211738 21 381211 0.0008004 ***
## shrub_density:aridity 1 4465 20 376746 0.6263640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n1), "\n")## Linear Model AIC: 309.9796quadratic_model_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 139.405 100.932 -9.768
## aridity I(aridity^2) shrub_density:aridity
## -412.549 117.917 6.412
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 186500 AIC: 297.1anova(quadratic_model_1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.106605
## I(shrub_density^2) 1 2942 21 590007 0.594141
## aridity 1 261283 20 328724 5.12e-07 ***
## I(aridity^2) 1 89956 19 238768 0.003213 **
## shrub_density:aridity 1 52272 18 186496 0.024696 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_1), "\n")## Quadratic Model AIC: 297.1038if (AIC(quadratic_model_1) < AIC(n1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.abund_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, animals)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Richness
n2 <- glm(richness ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n2##
## Call: glm(formula = richness ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -0.34946 0.08662 2.58054
## shrub_density:aridity
## -0.01174
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 475
## Residual Deviance: 132.4 AIC: 119.1anova(n2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.43 22 468.53 0.3243
## aridity 1 335.94 21 132.58 1.043e-12 ***
## shrub_density:aridity 1 0.22 20 132.36 0.8546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n2), "\n")## Linear Model AIC: 119.0888quadratic_model_2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_2##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 1.9865 -0.7054 0.0776
## aridity I(aridity^2) shrub_density:aridity
## -3.8810 1.6566 -0.0559
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 475
## Residual Deviance: 87.2 AIC: 113.1anova(quadratic_model_2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.431 22 468.53 0.24925
## I(shrub_density^2) 1 106.430 21 362.10 2.769e-06 ***
## aridity 1 247.045 20 115.05 9.248e-13 ***
## I(aridity^2) 1 23.883 19 91.17 0.02639 *
## shrub_density:aridity 1 3.972 18 87.20 0.36517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")## Quadratic Model AIC: 113.0717if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.rich_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, richness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Evenness
n3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
n3##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.019618 -0.001800 0.030276
## shrub_density:aridity
## 0.001651
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.05188 AIC: -69.18anova(n3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.0927 .
## aridity 1 0.081917 21 0.056269 1.913e-08 ***
## shrub_density:aridity 1 0.004391 20 0.051878 0.1932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(n3), "\n")## Linear Model AIC: -69.17676quadratic_model_3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_3##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.0446964 -0.0295177 0.0027085
## aridity I(aridity^2) shrub_density:aridity
## -0.0385879 0.0178654 0.0002091
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.03225 AIC: -76.59anova(quadratic_model_3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.04307 *
## I(shrub_density^2) 1 0.056383 21 0.081804 2.027e-08 ***
## aridity 1 0.047100 20 0.034703 2.941e-07 ***
## I(aridity^2) 1 0.002397 19 0.032306 0.24743
## shrub_density:aridity 1 0.000056 18 0.032251 0.86020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")## Quadratic Model AIC: 113.0717if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Quadratic model is better.even_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
plot <- abund_dens_2022/rich_dens_2022/even_dens_2022
plot
### Temperature (Hold off on this and ask chris)
t1 <- glm(animals ~ shrub_density * mean_temp, family = "gaussian", data = final_data_2022)
t1##
## Call: glm(formula = animals ~ shrub_density * mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density mean_temp
## 641.3669 22.3664 -20.8447
## shrub_density:mean_temp
## -0.6221
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 389100 AIC: 310.8anova(t1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.238985
## mean_temp 1 199846 21 393103 0.001351 **
## shrub_density:mean_temp 1 3970 20 389134 0.651496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(t1), "\n")## Linear Model AIC: 310.756quadratic_temp_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", data = final_data_2022)
quadratic_temp_1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 4363.574 155.190 -6.801
## mean_temp I(mean_temp^2) shrub_density:mean_temp
## -305.916 5.317 -2.618
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 301200 AIC: 308.6anova(quadratic_temp_1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2042037
## I(shrub_density^2) 1 2942 21 590007 0.6750263
## mean_temp 1 240180 20 349827 0.0001516 ***
## I(mean_temp^2) 1 2772 19 347055 0.6840117
## shrub_density:mean_temp 1 45820 18 301235 0.0979909 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_temp_1), "\n")## Quadratic Model AIC: 308.6113Shrub Density 2023 (Linear better for 2023)
final_data_2023 <- final_data %>%
filter(year == "2023")
x1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2023)
x1##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 8.32431 1.85904 2.30955
## shrub_density:aridity
## -0.05488
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 12650
## Residual Deviance: 10190 AIC: 207.5anova(x1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12649
## shrub_density 1 2257.72 20 10391 0.04579 *
## aridity 1 199.04 19 10192 0.55317
## shrub_density:aridity 1 4.64 18 10187 0.92782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x1), "\n")## Linear Model AIC: 207.4663quadratic_model_x1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2023)
quadratic_model_x1##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## -1.65760 3.43942 -0.15541
## aridity I(aridity^2) shrub_density:aridity
## 29.97243 -7.06338 0.03991
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 12650
## Residual Deviance: 9619 AIC: 210.2anova(quadratic_model_x1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12648.8
## shrub_density 1 2257.72 20 10391.1 0.05264 .
## I(shrub_density^2) 1 55.46 19 10335.6 0.76135
## aridity 1 317.46 18 10018.1 0.46743
## I(aridity^2) 1 396.75 17 9621.4 0.41659
## shrub_density:aridity 1 1.93 16 9619.5 0.95482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x1), "\n")## Quadratic Model AIC: 210.2043if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.abund_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, animals)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
abund_dens_2023## `geom_smooth()` using formula = 'y ~ x'
x2 <- glm(richness ~ shrub_density * aridity, data = final_data_2023)
x2##
## Call: glm(formula = richness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 4.775057 0.220144 -0.167510
## shrub_density:aridity
## -0.009058
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 95.32
## Residual Deviance: 67.28 AIC: 97.03anova(x2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.009006 **
## aridity 1 2.4110 19 67.409 0.421898
## shrub_density:aridity 1 0.1265 18 67.282 0.854019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x2), "\n")## Linear Model AIC: 97.02606quadratic_model_x2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x2##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 3.32139 -0.05358 0.02657
## aridity I(aridity^2) shrub_density:aridity
## 3.87515 -1.02486 -0.02127
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 95.32
## Residual Deviance: 59.25 AIC: 98.23anova(quadratic_model_x2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.008689 **
## I(shrub_density^2) 1 0.4031 19 69.417 0.741448
## aridity 1 2.0180 18 67.399 0.460389
## I(aridity^2) 1 7.6014 17 59.797 0.151936
## shrub_density:aridity 1 0.5479 16 59.249 0.700486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x2), "\n")## Quadratic Model AIC: 98.22897if (AIC(quadratic_model_x2) < AIC(x2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.rich_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, richness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
rich_dens_2023## `geom_smooth()` using formula = 'y ~ x'
x3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
x3##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.1816823 0.0084948 -0.0469045
## shrub_density:aridity
## -0.0009284
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.009233 AIC: -98.64anova(x3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 2.33e-07 ***
## aridity 1 0.136519 19 0.010562 < 2.2e-16 ***
## shrub_density:aridity 1 0.001329 18 0.009233 0.1074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Linear Model AIC:", AIC(x3), "\n")## Linear Model AIC: -98.63904quadratic_model_x3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x3##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.1963905 0.0105327 -0.0001974
## aridity I(aridity^2) shrub_density:aridity
## -0.0877876 0.0103750 -0.0008426
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.008405 AIC: -96.71anova(quadratic_model_x3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 3.226e-07 ***
## I(shrub_density^2) 1 0.020159 19 0.126922 5.833e-10 ***
## aridity 1 0.116561 18 0.010361 < 2.2e-16 ***
## I(aridity^2) 1 0.001096 17 0.009265 0.1485
## shrub_density:aridity 1 0.000860 16 0.008405 0.2006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cat("Quadratic Model AIC:", AIC(quadratic_model_x3), "\n")## Quadratic Model AIC: -96.7066if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}## Linear model is better or equally good.even_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
even_dens_2023## `geom_smooth()` using formula = 'y ~ x'
plot2 <- abund_dens_2023/rich_dens_2023/even_dens_2023
plot2## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'